diff --git a/extractor-api-lib/.gitignore b/extractor-api-lib/.gitignore
index a77bf09..f0ff067 100644
--- a/extractor-api-lib/.gitignore
+++ b/extractor-api-lib/.gitignore
@@ -4,6 +4,7 @@
 __pycache__/
 *.py[cod]
 *$py.class
+**/.DS_Store
 
 # C extensions
 *.so
diff --git a/extractor-api-lib/poetry.lock b/extractor-api-lib/poetry.lock
index a1bc91d..bda6fc5 100644
--- a/extractor-api-lib/poetry.lock
+++ b/extractor-api-lib/poetry.lock
@@ -26,97 +26,102 @@ files = [
 
 [[package]]
 name = "aiohttp"
-version = "3.11.18"
+version = "3.12.12"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
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 ]
 
 [package.dependencies]
-aiohappyeyeballs = ">=2.3.0"
+aiohappyeyeballs = ">=2.5.0"
 aiosignal = ">=1.1.2"
 attrs = ">=17.3.0"
 frozenlist = ">=1.1.1"
@@ -125,7 +130,7 @@ propcache = ">=0.2.0"
 yarl = ">=1.17.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli ; platform_python_implementation == \"CPython\"", "aiodns (>=3.2.0) ; sys_platform == \"linux\" or sys_platform == \"darwin\"", "brotlicffi ; platform_python_implementation != \"CPython\""]
+speedups = ["Brotli ; platform_python_implementation == \"CPython\"", "aiodns (>=3.3.0)", "brotlicffi ; platform_python_implementation != \"CPython\""]
 
 [[package]]
 name = "aiosignal"
@@ -201,14 +206,14 @@ files = [
 
 [[package]]
 name = "atlassian-python-api"
-version = "4.0.3"
+version = "4.0.4"
 description = "Python Atlassian REST API Wrapper"
 optional = false
 python-versions = "*"
 groups = ["main"]
 files = [
-    {file = "atlassian_python_api-4.0.3-py3-none-any.whl", hash = "sha256:23d9f0d33af260bbe7cec7edde125ce40b370183da7bf2f1c012ff5cfe721fa0"},
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+    {file = "atlassian_python_api-4.0.4-py3-none-any.whl", hash = "sha256:d5e61a4a6a69e9f9b0737f449fad6beb5e4dc39665ac4269f1f8fa8fa4246c3c"},
+    {file = "atlassian_python_api-4.0.4.tar.gz", hash = "sha256:351e378cf489f31c3fbf9e71670fa7ee2a7b68c20d616665d4e8bca17a3d2983"},
 ]
 
 [package.dependencies]
@@ -218,7 +223,6 @@ jmespath = "*"
 oauthlib = "*"
 requests = "*"
 requests_oauthlib = "*"
-six = "*"
 typing-extensions = "*"
 
 [package.extras]
@@ -351,34 +355,34 @@ uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "boto3"
-version = "1.38.10"
+version = "1.38.34"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
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 jmespath = ">=0.7.1,<2.0.0"
-s3transfer = ">=0.12.0,<0.13.0"
+s3transfer = ">=0.13.0,<0.14.0"
 
 [package.extras]
 crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
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 [package.dependencies]
@@ -389,6 +393,35 @@ urllib3 = {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >
 [package.extras]
 crt = ["awscrt (==0.23.8)"]
 
+[[package]]
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+version = "1.0.0"
+description = "PDF Table Extraction for Humans."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
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+pypdfium2 = ">=4"
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+
+[package.extras]
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+
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 version = "2025.4.26"
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 description = "Composable command line interface toolkit"
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 [package.extras]
@@ -701,62 +738,62 @@ toml = ["tomli ; python_full_version <= \"3.11.0a6\""]
 
 [[package]]
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 [package.dependencies]
-cffi = {version = ">=1.12", markers = "platform_python_implementation != \"PyPy\""}
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 [package.extras]
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 sdist = ["build (>=1.0.0)"]
 ssh = ["bcrypt (>=3.1.5)"]
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 test-randomorder = ["pytest-randomly"]
 
 [[package]]
@@ -777,18 +814,17 @@ typing-inspect = ">=0.4.0,<1"
 
 [[package]]
 name = "datasets"
-version = "3.5.1"
+version = "3.6.0"
 description = "HuggingFace community-driven open-source library of datasets"
 optional = false
 python-versions = ">=3.9.0"
 groups = ["main"]
 files = [
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 [package.dependencies]
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 dill = ">=0.3.0,<0.3.9"
 filelock = "*"
 fsspec = {version = ">=2023.1.0,<=2025.3.0", extras = ["http"]}
@@ -806,7 +842,7 @@ xxhash = "*"
 [package.extras]
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 benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
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 jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
 pdfs = ["pdfplumber (>=0.11.4)"]
@@ -814,8 +850,8 @@ quality = ["ruff (>=0.3.0)"]
 s3 = ["s3fs"]
 tensorflow = ["tensorflow (>=2.6.0)"]
 tensorflow-gpu = ["tensorflow (>=2.6.0)"]
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 torch = ["torch"]
 vision = ["Pillow (>=9.4.0)"]
 
@@ -857,94 +893,42 @@ files = [
 
 [[package]]
 name = "dependency-injector"
-version = "4.46.0"
+version = "4.47.1"
 description = "Dependency injection framework for Python"
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-python-versions = ">=3.7"
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 groups = ["main"]
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 version = "2.2.0"
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 [[package]]
@@ -1602,68 +1583,67 @@ tqdm = ["tqdm"]
 
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+]
+
+[package.extras]
+tests = ["pytest"]
+
 [[package]]
 name = "html5lib"
 version = "1.1"
@@ -1765,19 +1767,20 @@ files = [
 
 [[package]]
 name = "huggingface-hub"
-version = "0.30.2"
+version = "0.32.6"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 groups = ["main"]
 files = [
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+    {file = "huggingface_hub-0.32.6-py3-none-any.whl", hash = "sha256:32cde9558c965477556edca72352621def7fbc42e167aaf33f4cdb9af65bb28b"},
+    {file = "huggingface_hub-0.32.6.tar.gz", hash = "sha256:8e960f23dc57519c6c2a0bbc7e9bc030eaa14e7f2d61f8e68fd3d025dabed2fa"},
 ]
 
 [package.dependencies]
 filelock = "*"
 fsspec = ">=2023.5.0"
+hf-xet = {version = ">=1.1.2,<2.0.0", markers = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\""}
 packaging = ">=20.9"
 pyyaml = ">=5.1"
 requests = "*"
@@ -1785,17 +1788,19 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
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-hf-xet = ["hf-xet (>=0.1.4)"]
+hf-xet = ["hf-xet (>=1.1.2,<2.0.0)"]
 inference = ["aiohttp"]
-quality = ["libcst (==1.4.0)", "mypy (==1.5.1)", "ruff (>=0.9.0)"]
+mcp = ["aiohttp", "mcp (>=1.8.0)", "typer"]
+oauth = ["authlib (>=1.3.2)", "fastapi", "httpx", "itsdangerous"]
+quality = ["libcst (==1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "ruff (>=0.9.0)"]
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 tensorflow-testing = ["keras (<3.0)", "tensorflow"]
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 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
@@ -1840,14 +1845,14 @@ files = [
 
 [[package]]
 name = "joblib"
-version = "1.5.0"
+version = "1.5.1"
 description = "Lightweight pipelining with Python functions"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
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 ]
 
 [[package]]
@@ -1919,22 +1924,22 @@ xai = ["langchain-xai"]
 
 [[package]]
 name = "langchain-community"
-version = "0.3.23"
+version = "0.3.24"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
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+    {file = "langchain_community-0.3.24.tar.gz", hash = "sha256:62d9e8cf9aadf35182ec3925f9ec1c8e5e84fb4f199f67a01aee496d289dc264"},
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-langchain = ">=0.3.24,<1.0.0"
-langchain-core = ">=0.3.56,<1.0.0"
+langchain = ">=0.3.25,<1.0.0"
+langchain-core = ">=0.3.59,<1.0.0"
 langsmith = ">=0.1.125,<0.4"
 numpy = {version = ">=2.1.0", markers = "python_version >= \"3.13\""}
 pydantic-settings = ">=2.4.0,<3.0.0"
@@ -1996,14 +2001,14 @@ six = "*"
 
 [[package]]
 name = "langsmith"
-version = "0.3.42"
+version = "0.3.45"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
@@ -2257,116 +2262,116 @@ files = [
 
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 [[package]]
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 ]
 
 [package.dependencies]
@@ -2947,14 +2983,14 @@ xmp = ["defusedxml"]
 
 [[package]]
 name = "platformdirs"
-version = "4.3.7"
+version = "4.3.8"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
 python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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 ]
 
 [package.extras]
@@ -2964,126 +3000,126 @@ type = ["mypy (>=1.14.1)"]
 
 [[package]]
 name = "pluggy"
-version = "1.5.0"
+version = "1.6.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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 ]
 
 [package.extras]
 dev = ["pre-commit", "tox"]
-testing = ["pytest", "pytest-benchmark"]
+testing = ["coverage", "pytest", "pytest-benchmark"]
 
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 name = "propcache"
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 [[package]]
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-version = "2.11.4"
+version = "2.11.5"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
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 [package.dependencies]
@@ -3404,20 +3440,20 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pypdf"
-version = "5.4.0"
+version = "5.6.0"
 description = "A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files"
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 python-versions = ">=3.8"
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 [package.extras]
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 cryptodome = ["PyCryptodome"]
-dev = ["black", "flit", "pip-tools", "pre-commit (<2.18.0)", "pytest-cov", "pytest-socket", "pytest-timeout", "pytest-xdist", "wheel"]
+dev = ["black", "flit", "pip-tools", "pre-commit", "pytest-cov", "pytest-socket", "pytest-timeout", "pytest-xdist", "wheel"]
 docs = ["myst_parser", "sphinx", "sphinx_rtd_theme"]
 full = ["Pillow (>=8.0.0)", "cryptography"]
 image = ["Pillow (>=8.0.0)"]
@@ -3463,24 +3499,44 @@ Pillow = ">=8.0.0"
 
 [[package]]
 name = "pytest"
-version = "8.3.5"
+version = "8.4.0"
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 [package.dependencies]
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-iniconfig = "*"
-packaging = "*"
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+iniconfig = ">=1"
+packaging = ">=20"
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+pygments = ">=2.7.2"
+
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+
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+optional = false
+python-versions = ">=3.9"
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 [package.extras]
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+docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1)"]
+testing = ["coverage (>=6.2)", "hypothesis (>=5.7.1)"]
 
 [[package]]
 name = "python-dateutil"
@@ -3890,19 +3946,19 @@ files = [
 
 [[package]]
 name = "requests"
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 [package.dependencies]
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-charset-normalizer = ">=2,<4"
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 idna = ">=2.5,<4"
 urllib3 = ">=1.21.1,<3"
 
@@ -3965,14 +4021,14 @@ jupyter = ["ipywidgets (>=7.5.1,<9)"]
 
 [[package]]
 name = "s3transfer"
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+version = "0.13.0"
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 python-versions = ">=3.9"
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 [package.dependencies]
@@ -3983,14 +4039,14 @@ crt = ["botocore[crt] (>=1.37.4,<2.0a.0)"]
 
 [[package]]
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 [package.extras]
@@ -4040,69 +4096,69 @@ files = [
 
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 [package.dependencies]
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 [[package]]
@@ -4250,14 +4306,14 @@ typing-extensions = ">=3.7.4"
 
 [[package]]
 name = "typing-inspection"
-version = "0.4.0"
+version = "0.4.1"
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 [package.dependencies]
@@ -4335,26 +4391,24 @@ xlsx = ["networkx", "openpyxl", "pandas", "xlrd"]
 
 [[package]]
 name = "unstructured-client"
-version = "0.34.0"
+version = "0.36.0"
 description = "Python Client SDK for Unstructured API"
 optional = false
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+python-versions = ">=3.9.2"
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 [[package]]
 name = "webencodings"
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+    {file = "yarl-1.20.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:793fd0580cb9664548c6b83c63b43c477212c0260891ddf86809e1c06c8b08f1"},
+    {file = "yarl-1.20.1-cp313-cp313-win32.whl", hash = "sha256:468f6e40285de5a5b3c44981ca3a319a4b208ccc07d526b20b12aeedcfa654b7"},
+    {file = "yarl-1.20.1-cp313-cp313-win_amd64.whl", hash = "sha256:495b4ef2fea40596bfc0affe3837411d6aa3371abcf31aac0ccc4bdd64d4ef5c"},
+    {file = "yarl-1.20.1-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:f60233b98423aab21d249a30eb27c389c14929f47be8430efa7dbd91493a729d"},
+    {file = "yarl-1.20.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:6f3eff4cc3f03d650d8755c6eefc844edde99d641d0dcf4da3ab27141a5f8ddf"},
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+    {file = "yarl-1.20.1-cp313-cp313t-manylinux_2_17_armv7l.manylinux2014_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:8e0fe9364ad0fddab2688ce72cb7a8e61ea42eff3c7caeeb83874a5d479c896c"},
+    {file = "yarl-1.20.1-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8f64fbf81878ba914562c672024089e3401974a39767747691c65080a67b18c1"},
+    {file = "yarl-1.20.1-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f6342d643bf9a1de97e512e45e4b9560a043347e779a173250824f8b254bd5ce"},
+    {file = "yarl-1.20.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:56dac5f452ed25eef0f6e3c6a066c6ab68971d96a9fb441791cad0efba6140d3"},
+    {file = "yarl-1.20.1-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c7d7f497126d65e2cad8dc5f97d34c27b19199b6414a40cb36b52f41b79014be"},
+    {file = "yarl-1.20.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:67e708dfb8e78d8a19169818eeb5c7a80717562de9051bf2413aca8e3696bf16"},
+    {file = "yarl-1.20.1-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:595c07bc79af2494365cc96ddeb772f76272364ef7c80fb892ef9d0649586513"},
+    {file = "yarl-1.20.1-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:7bdd2f80f4a7df852ab9ab49484a4dee8030023aa536df41f2d922fd57bf023f"},
+    {file = "yarl-1.20.1-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:c03bfebc4ae8d862f853a9757199677ab74ec25424d0ebd68a0027e9c639a390"},
+    {file = "yarl-1.20.1-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:344d1103e9c1523f32a5ed704d576172d2cabed3122ea90b1d4e11fe17c66458"},
+    {file = "yarl-1.20.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:88cab98aa4e13e1ade8c141daeedd300a4603b7132819c484841bb7af3edce9e"},
+    {file = "yarl-1.20.1-cp313-cp313t-win32.whl", hash = "sha256:b121ff6a7cbd4abc28985b6028235491941b9fe8fe226e6fdc539c977ea1739d"},
+    {file = "yarl-1.20.1-cp313-cp313t-win_amd64.whl", hash = "sha256:541d050a355bbbc27e55d906bc91cb6fe42f96c01413dd0f4ed5a5240513874f"},
+    {file = "yarl-1.20.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:e42ba79e2efb6845ebab49c7bf20306c4edf74a0b20fc6b2ccdd1a219d12fad3"},
+    {file = "yarl-1.20.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:41493b9b7c312ac448b7f0a42a089dffe1d6e6e981a2d76205801a023ed26a2b"},
+    {file = "yarl-1.20.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:f5a5928ff5eb13408c62a968ac90d43f8322fd56d87008b8f9dabf3c0f6ee983"},
+    {file = "yarl-1.20.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:30c41ad5d717b3961b2dd785593b67d386b73feca30522048d37298fee981805"},
+    {file = "yarl-1.20.1-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:59febc3969b0781682b469d4aca1a5cab7505a4f7b85acf6db01fa500fa3f6ba"},
+    {file = "yarl-1.20.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d2b6fb3622b7e5bf7a6e5b679a69326b4279e805ed1699d749739a61d242449e"},
+    {file = "yarl-1.20.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:749d73611db8d26a6281086f859ea7ec08f9c4c56cec864e52028c8b328db723"},
+    {file = "yarl-1.20.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9427925776096e664c39e131447aa20ec738bdd77c049c48ea5200db2237e000"},
+    {file = "yarl-1.20.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ff70f32aa316393eaf8222d518ce9118148eddb8a53073c2403863b41033eed5"},
+    {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:c7ddf7a09f38667aea38801da8b8d6bfe81df767d9dfc8c88eb45827b195cd1c"},
+    {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_armv7l.whl", hash = "sha256:57edc88517d7fc62b174fcfb2e939fbc486a68315d648d7e74d07fac42cec240"},
+    {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:dab096ce479d5894d62c26ff4f699ec9072269d514b4edd630a393223f45a0ee"},
+    {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:14a85f3bd2d7bb255be7183e5d7d6e70add151a98edf56a770d6140f5d5f4010"},
+    {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:2c89b5c792685dd9cd3fa9761c1b9f46fc240c2a3265483acc1565769996a3f8"},
+    {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:69e9b141de5511021942a6866990aea6d111c9042235de90e08f94cf972ca03d"},
+    {file = "yarl-1.20.1-cp39-cp39-win32.whl", hash = "sha256:b5f307337819cdfdbb40193cad84978a029f847b0a357fbe49f712063cfc4f06"},
+    {file = "yarl-1.20.1-cp39-cp39-win_amd64.whl", hash = "sha256:eae7bfe2069f9c1c5b05fc7fe5d612e5bbc089a39309904ee8b829e322dcad00"},
+    {file = "yarl-1.20.1-py3-none-any.whl", hash = "sha256:83b8eb083fe4683c6115795d9fc1cfaf2cbbefb19b3a1cb68f6527460f483a77"},
+    {file = "yarl-1.20.1.tar.gz", hash = "sha256:d017a4997ee50c91fd5466cef416231bb82177b93b029906cefc542ce14c35ac"},
 ]
 
 [package.dependencies]
@@ -4889,4 +4943,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.1"
 python-versions = "^3.13"
-content-hash = "6ce3a0cec80ac06536113e984e478ebc8f3e398ba1226c0d6b920814d4796b49"
+content-hash = "e9e3c53994b363d8e8f002503800a29fae169ec568ffbcc99d7a745d4855a8ac"
diff --git a/extractor-api-lib/pyproject.toml b/extractor-api-lib/pyproject.toml
index 406f42b..3daf933 100644
--- a/extractor-api-lib/pyproject.toml
+++ b/extractor-api-lib/pyproject.toml
@@ -9,8 +9,13 @@ description = "Extracts the content of documents, websites, etc and maps it to a
 authors = ["STACKIT Data and AI Consulting <data-ai-consulting@stackit.cloud>"]
 packages = [{ include = "extractor_api_lib", from = "src" }]
 
+[[tool.poetry.source]]
+name = "pytorch_cpu"
+url = "https://download.pytorch.org/whl/cpu"
+priority = "explicit"
+
 [tool.flake8]
-exclude = [".eggs", "./src/extractor_api_lib/models/*", ".git", ".hg", ".mypy_cache", ".tox", ".venv", ".devcontainer", "venv", "_build", "buck-out", "build", "dist", "**/__init__.py"]
+exclude = [".eggs", "./src/extractor_api_lib/models/*", ".git", ".hg", ".mypy_cache", ".tox", ".venv", ".devcontainer", "venv", "_build", "buck-out", "build", "dist", "**/__init__.py", "tests/test_data/generate_test_pdfs.py"]
 statistics = true
 show-source = false
 max-complexity = 10
@@ -93,10 +98,12 @@ langchain-community = "^0.3.23"
 atlassian-python-api = "^4.0.3"
 markdownify = "^1.1.0"
 langchain-core = "0.3.63"
+camelot-py = {extras = ["cv"], version = "^1.0.0"}
 fake-useragent = "^2.2.0"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.3.5"
+pytest-asyncio = "^0.26.0"
 coverage = "^7.8.0"
 flake8 = "^7.2.0"
 flake8-black = "^0.3.6"
diff --git a/extractor-api-lib/src/extractor_api_lib/impl/extractors/file_extractors/pdf_extractor.py b/extractor-api-lib/src/extractor_api_lib/impl/extractors/file_extractors/pdf_extractor.py
index 928998f..3234077 100644
--- a/extractor-api-lib/src/extractor_api_lib/impl/extractors/file_extractors/pdf_extractor.py
+++ b/extractor-api-lib/src/extractor_api_lib/impl/extractors/file_extractors/pdf_extractor.py
@@ -13,6 +13,8 @@
 import pytesseract
 from pdf2image import convert_from_path
 from pdfplumber.page import Page
+from langdetect import detect
+import camelot
 
 
 from extractor_api_lib.impl.settings.pdf_extractor_settings import PDFExtractorSettings
@@ -25,6 +27,7 @@
 from extractor_api_lib.extractors.information_file_extractor import InformationFileExtractor
 
 logger = logging.getLogger(__name__)
+logging.getLogger("pdfminer").setLevel(logging.WARNING)
 
 
 class PDFExtractor(InformationFileExtractor):
@@ -39,10 +42,13 @@ class PDFExtractor(InformationFileExtractor):
         Regular expression pattern to identify titles in the text.document
     TITLE_PATTERN_MULTILINE : re.Pattern
         Regular expression pattern to identify titles in the text with multiline support.
+    TEXT_THRESHOLD : float
+        Threshold for determining if a page has extractable text (default: 50 characters).
     """
 
     TITLE_PATTERN = re.compile(r"(^|\n)(\d+\.[\.\d]*[\t ][a-zA-Z0-9 äöüÄÖÜß\-]+)")
     TITLE_PATTERN_MULTILINE = re.compile(r"(^|\n)(\d+\.[\.\d]*[\t ][a-zA-Z0-9 äöüÄÖÜß\-]+)", re.MULTILINE)
+    TEXT_THRESHOLD = 50  # Minimum characters to consider page as text-based
 
     def __init__(
         self,
@@ -65,6 +71,10 @@ def __init__(
         self._dataframe_converter = dataframe_converter
         self._settings = pdf_extractor_settings
         self.old_image_id = None
+        self._lang_map = {
+            "en": "eng",
+            "de": "deu",
+        }
 
     @property
     def compatible_file_types(self) -> list[FileType]:
@@ -87,13 +97,14 @@ def _create_information_piece(
         content_type: ContentType,
         information_id: str,
         additional_meta: Optional[dict] = None,
+        related_ids: list[str] = None,
     ) -> InternalInformationPiece:
         metadata = {
             "document": document_name,
             "page": page,
             "title": title,
             "id": information_id,
-            "related": [],
+            "related": related_ids if related_ids else [],
         }
         if additional_meta:
             metadata = metadata | additional_meta
@@ -118,32 +129,58 @@ async def aextract_content(self, file_path: Path, name: str) -> list[InternalInf
         list[InformationPiece]
             The extracted information.
         """
-        # Step 1: Convert each PDF page to an image
         images = convert_from_path(file_path)
 
         with tempfile.TemporaryDirectory() as temp_dir:
             for i, image in enumerate(images, 1):
                 image.save(Path(temp_dir, f"page_{i}.png").as_posix(), "PNG")
 
-            # Step 2: Use pdfplumber to identify table/figure coordinates and extract text
             pdf_elements = []
 
             current_title = ""
             with pdfplumber.open(file_path) as pdf:
                 for page_idx, page in enumerate(pdf.pages, 1):
-                    logger.debug("Processing page: %d" % page_idx)
+                    logger.debug("Processing page: %d/%d", page_idx, len(pdf.pages))
+
+                    is_text_based = self._is_text_based(page)
+
                     (new_pdf_elements, current_title) = self._extract_content_from_page(
                         page_index=page_idx,
                         page=page,
+                        is_text_based=is_text_based,
                         temp_dir=temp_dir,
                         title=current_title,
                         document_name=name,
                     )
                     pdf_elements += new_pdf_elements
 
+        logger.info(f"Extraction completed. Found {len(pdf_elements)} information pieces.")
         return pdf_elements
 
-    def _extract_tabluar_data(
+    def _is_text_based(self, page: Page) -> bool:
+        """Classify whether a page is text-based, scanned.
+
+        Parameters
+        ----------
+        page : Page
+            The pdfplumber page object.
+
+        Returns
+        -------
+        bool
+            returns True if the page is text-based, False if it is scanned.
+        """
+        # Try to extract text using pdfplumber
+        extractable_text = page.extract_text() or ""
+
+        # Clean and count meaningful text
+        meaningful_text = re.sub(r"\s+", " ", extractable_text.strip())
+
+        if len(meaningful_text) >= self.TEXT_THRESHOLD:
+            return True
+        return False
+
+    def _extract_tables_from_text_page(
         self,
         page: Page,
         page_index: int,
@@ -151,46 +188,56 @@ def _extract_tabluar_data(
         text_x_tolerance: int = 1,
         text_y_tolerance: int = 1,
     ) -> list[InternalInformationPiece]:
-        return_value = []
-        pdfplumber_tables = page.find_tables()
-        table_strings = []
-        for table in pdfplumber_tables:
-            table_df = pd.DataFrame(table.extract(x_tolerance=text_x_tolerance, y_tolerance=text_y_tolerance))
-            try:
-                converted_table = self._dataframe_converter.convert(table_df)
-            except TypeError as e:
-                logger.error(f"Error while converting table to string: {e}")
-                continue
-            if converted_table == "":
-                continue
-            table_strings.append(self._dataframe_converter.convert(table_df))
-
-        if table_strings:
-            for table_content in table_strings:
-                return_value.append(
+        table_elements = []
+
+        try:
+            pdfplumber_tables = page.find_tables()
+
+            for idx, table in enumerate(pdfplumber_tables):
+                table_data = table.extract(x_tolerance=text_x_tolerance, y_tolerance=text_y_tolerance)
+                if not table_data or all(not row or all(not cell for cell in row) for row in table_data):
+                    continue
+                table_df = pd.DataFrame(table_data)
+                try:
+                    converted_table = self._dataframe_converter.convert(table_df)
+                except TypeError as e:
+                    logger.error(f"Error while converting table to string: {e}")
+                    continue
+                if not converted_table.strip():
+                    continue
+                table_elements.append(
                     self._create_information_piece(
                         document_name,
                         page_index,
-                        "",
-                        table_content,
+                        f"Table {idx + 1}",
+                        converted_table,
                         ContentType.TABLE,
                         information_id=hash_datetime(),
                     )
                 )
+        except Exception as e:
+            logger.warning(f"Failed to find tables on page {page_index}: {e}")
+
+        return table_elements
 
-        return return_value
+    def _auto_detect_language(self, text):
+        try:
+            return detect(text)
+        except Exception:
+            return "en"
 
-    def _extract_text(
+    def _extract_text_from_scanned_page(
         self,
         page: Page,
         scale_x: int,
         scale_y: int,
         image: np.ndarray,
         pdf_page_height: int,
-        pdf_page_width: int,
-    ) -> str:
+        with_image_masking: Optional[bool] = True,
+    ) -> tuple[str, bytes]:
         thickness = -1
         color = (255, 255, 255)
+        original_image = image.copy()
         for table in page.find_tables():
             table_coords = table.bbox
             start_point = (
@@ -204,45 +251,100 @@ def _extract_text(
 
             cv2.rectangle(image, start_point, end_point, color, thickness)
 
-        imgs = page.images
-        for img in imgs:
-            start_point = (
-                int(img["x0"] * scale_x),
-                int((pdf_page_height - img["y1"]) * scale_y),
-            )
-            end_point = (
-                int(img["x1"] * scale_x),
-                int((pdf_page_height - img["y0"]) * scale_y),
+        if with_image_masking:
+            for img in page.images:
+                start_point = (
+                    int(img["x0"] * scale_x),
+                    int((pdf_page_height - img["y1"]) * scale_y),
+                )
+                end_point = (
+                    int(img["x1"] * scale_x),
+                    int((pdf_page_height - img["y0"]) * scale_y),
+                )
+                cv2.rectangle(image, start_point, end_point, color, thickness)
+
+        rough_text = pytesseract.image_to_string(image, lang="eng")
+        if not rough_text and with_image_masking:
+            return self._extract_text_from_scanned_page(
+                page, scale_x, scale_y, original_image, pdf_page_height, with_image_masking=False
             )
-            cv2.rectangle(image, start_point, end_point, color, thickness)
-        lx = 0
-        ly = 0
-        start_point = (lx, ly)
-        ux = int(pdf_page_width * scale_x)
-        uy = 150
-        end_point = (ux, uy)
+        lang_code = self._auto_detect_language(rough_text)
+        tesseract_lang = self._lang_map.get(lang_code, "eng")
+        pdf_bytes = pytesseract.image_to_pdf_or_hocr(original_image, extension="pdf", lang=tesseract_lang)
+        if lang_code == "en":
+            return (rough_text, pdf_bytes)
 
-        cv2.rectangle(image, start_point, end_point, color, thickness)
+        return (pytesseract.image_to_string(image, lang=tesseract_lang), pdf_bytes)
 
-        ly = int(pdf_page_height * scale_y - 160)
-        start_point = (lx, ly)
-        ux = int(pdf_page_width * scale_x)
-        uy = int(pdf_page_height * scale_y)
-        end_point = (ux, uy)
+    def _extract_tables_from_scanned_page(
+        self, page_index: int, document_name: str, filename: str
+    ) -> list[InternalInformationPiece]:
+        """Extract tables from scanned page using multiple methods.
 
-        cv2.rectangle(image, start_point, end_point, color, thickness)
+        Parameters
+        ----------
+        page_index : int
+            The page number.
+        document_name : str
+            Name of the document.
+        filename: str
+            Path to the PDF file including the filename.
 
-        return pytesseract.image_to_string(image, lang="deu")
+        Returns
+        -------
+        list[InternalInformationPiece]
+            List of extracted table information pieces.
+        """
+        table_elements = []
+
+        # Method 1: Try Camelot (good for scanned tables)
+        try:
+            camelot_tables = camelot.read_pdf(str(filename))
+
+            for i, table in enumerate(camelot_tables):
+                if table.accuracy > 50:  # Only use tables with good accuracy
+                    try:
+                        converted_table = self._dataframe_converter.convert(table.df)
+                        if converted_table and converted_table.strip():
+                            table_elements.append(
+                                self._create_information_piece(
+                                    document_name,
+                                    page_index,
+                                    f"Table {i + 1}",
+                                    converted_table,
+                                    ContentType.TABLE,
+                                    information_id=hash_datetime(),
+                                    additional_meta={
+                                        "table_method": "camelot",
+                                        "accuracy": table.accuracy,
+                                        "table_index": i,
+                                    },
+                                )
+                            )
+                    except Exception as e:
+                        logger.warning(f"Failed to convert Camelot table {i + 1}: {e}")
+
+        except Exception as e:
+            logger.debug(f"Camelot table extraction failed for page {page_index}: {e}")
+
+        return table_elements
+
+    def _extract_text_from_text_page(self, page: Page) -> str:
+        try:
+            return page.extract_text() or ""
+        except Exception as e:
+            logger.warning(f"Failed to extract text with pdfplumber: {e}")
+            return ""
 
     def _extract_content_from_page(
         self,
         page_index: int,
         page: Page,
+        is_text_based: bool,
         temp_dir: str,
         title: str,
         document_name: str,
-    ):
-        pdf_elements = []
+    ) -> tuple[list[InternalInformationPiece], str]:
 
         im_path = Path(temp_dir, f"page_{page_index}.png")
         image = cv2.imread(im_path.as_posix())
@@ -252,44 +354,103 @@ def _extract_content_from_page(
         image_page_height = image.shape[0]
         scale_x = image_page_width / pdf_page_width
         scale_y = image_page_height / pdf_page_height
+        content = ""
+        table_elements = []
+        if is_text_based:
+            content = self._extract_text_from_text_page(page)
+            table_elements = self._extract_tables_from_text_page(
+                page=page, page_index=page_index, document_name=document_name
+            )
+        else:
+            content, pdf_bytes = self._extract_text_from_scanned_page(
+                page=page,
+                scale_x=scale_x,
+                scale_y=scale_y,
+                image=image,
+                pdf_page_height=pdf_page_height,
+            )
 
-        content = self._extract_text(
-            page=page,
-            scale_x=scale_x,
-            scale_y=scale_y,
-            image=image,
-            pdf_page_height=pdf_page_height,
-            pdf_page_width=pdf_page_width,
-        )
+            abs_filename = Path(temp_dir, f"page_{page_index}.pdf").as_posix()
+            with open(abs_filename, "wb") as f:
+                f.write(pdf_bytes)
+            table_elements = self._extract_tables_from_scanned_page(
+                page_index=page_index,
+                document_name=document_name,
+                filename=abs_filename,
+            )
+
+        text_element_ids = []
+        pdf_elements = []
+        if content and content.strip():
+            pdf_elements = self._process_text_content(content, title, page_index, document_name)
+            for element in pdf_elements:
+                text_element_ids.append(element.metadata["id"])
+
+        if table_elements:
+            tmp_table_elements = []
+            table_element_ids = []
+            for element in table_elements:
+                element.metadata["related"] = text_element_ids
+                tmp_table_elements.append(element)
+                table_element_ids.append(element.metadata["id"])
+            tmp_pdf_elements = []
+            for element in pdf_elements:
+                element.metadata["related"] = table_element_ids
+                tmp_pdf_elements.append(element)
+
+            pdf_elements = tmp_pdf_elements + tmp_table_elements
+
+        return pdf_elements, title
+
+    def _process_text_content(
+        self, content: str, title: str, page_index: int, document_name: str
+    ) -> list[InternalInformationPiece]:
+        """Process text content and split by titles.
 
-        if not content:
-            return [], title
+        Parameters
+        ----------
+        content : str
+            Raw text content.
+        title : str
+            Current title context.
+        page_index : int
+            The page number.
+        document_name : str
+            Name of the document.
+
+        Returns
+        -------
+        list[InternalInformationPiece]
+            List of processed text information pieces.
+        """
+        text_elements = []
 
+        if not content or not content.strip():
+            return text_elements
+
+        # Split content by title patterns
         content_array = re.split(self.TITLE_PATTERN_MULTILINE, content)
         content_array = [x.strip() for x in content_array if x and x.strip()]
 
+        current_title = title
+
         for content_item in content_array:
             is_title = re.match(self.TITLE_PATTERN, content_item)
             if is_title:
-                title = content_item
+                current_title = content_item.strip()
             else:
-                pdf_elements.append(
+                # Create text piece with current title context
+                full_content = f"{current_title}\n{content_item}" if current_title else content_item
+
+                text_elements.append(
                     self._create_information_piece(
                         document_name,
                         page_index,
-                        title,
-                        title + "\n" + content_item,
+                        current_title,
+                        full_content,
                         ContentType.TEXT,
                         information_id=hash_datetime(),
                     )
                 )
-        table_elements = self._extract_tabluar_data(
-            page=page,
-            page_index=page_index,
-            document_name=document_name,
-        )
 
-        if table_elements:
-            pdf_elements += table_elements
-
-        return pdf_elements, title
+        return text_elements
diff --git a/extractor-api-lib/tests/conftest.py b/extractor-api-lib/tests/conftest.py
index 90cfce0..0b7e23c 100644
--- a/extractor-api-lib/tests/conftest.py
+++ b/extractor-api-lib/tests/conftest.py
@@ -6,6 +6,7 @@
     SitemapLangchainDocument2InformationPiece,
 )
 from extractor_api_lib.impl.types.content_type import ContentType
+from extractor_api_lib.file_services.file_service import FileService
 
 
 @pytest.fixture
@@ -18,3 +19,9 @@ def mock_mapper():
         page_content="Test content",
     )
     return mapper
+
+
+@pytest.fixture
+def mock_file_service():
+    """Create a mock file service for testing."""
+    return MagicMock(spec=FileService)
diff --git a/extractor-api-lib/tests/pdf_extractor_test.py b/extractor-api-lib/tests/pdf_extractor_test.py
new file mode 100644
index 0000000..a353f8b
--- /dev/null
+++ b/extractor-api-lib/tests/pdf_extractor_test.py
@@ -0,0 +1,566 @@
+"""Comprehensive test suite for PDFExtractor class."""
+
+import time
+import logging
+
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+import pytest
+import pandas as pd
+import numpy as np
+from pdf2image.exceptions import PDFPageCountError
+import pdfplumber
+from pdf2image import convert_from_path
+
+from extractor_api_lib.impl.extractors.file_extractors.pdf_extractor import PDFExtractor
+from extractor_api_lib.impl.settings.pdf_extractor_settings import PDFExtractorSettings
+from extractor_api_lib.impl.types.content_type import ContentType
+from extractor_api_lib.impl.types.file_type import FileType
+from extractor_api_lib.models.dataclasses.internal_information_piece import InternalInformationPiece
+from extractor_api_lib.table_converter.dataframe_converter import DataframeConverter
+
+logger = logging.getLogger(__name__)
+
+
+class TestPDFExtractor:
+    """Test class for PDFExtractor."""
+
+    @pytest.fixture
+    def mock_pdf_extractor_settings(self):
+        """Create mock PDF extractor settings."""
+        return MagicMock(spec=PDFExtractorSettings)
+
+    @pytest.fixture
+    def mock_dataframe_converter(self):
+        """Create a mock dataframe converter."""
+        converter = MagicMock(spec=DataframeConverter)
+        converter.convert.return_value = "Mocked table content"
+        return converter
+
+    @pytest.fixture
+    def pdf_extractor(self, mock_file_service, mock_pdf_extractor_settings, mock_dataframe_converter):
+        """Create a PDFExtractor instance for testing."""
+        return PDFExtractor(
+            file_service=mock_file_service,
+            pdf_extractor_settings=mock_pdf_extractor_settings,
+            dataframe_converter=mock_dataframe_converter,
+        )
+
+    @pytest.fixture
+    def test_pdf_files(self):
+        """Provide paths to test PDF files."""
+        test_data_dir = Path(__file__).parent / "test_data"
+        return {
+            "text_based": test_data_dir / "text_based_document.pdf",
+            "scanned": test_data_dir / "scanned_document.pdf",
+            "mixed_content": test_data_dir / "mixed_content_document.pdf",
+            "multi_column": test_data_dir / "multi_column_document.pdf",
+        }
+
+    def test_compatible_file_types(self, pdf_extractor):
+        """Test that PDF extractor only supports PDF files."""
+        compatible_types = pdf_extractor.compatible_file_types
+        assert compatible_types == [FileType.PDF]
+
+    def test_create_information_piece(self):
+        """Test the static method for creating information pieces."""
+        piece = PDFExtractor._create_information_piece(
+            document_name="test_doc",
+            page=1,
+            title="Test Title",
+            content="Test content",
+            content_type=ContentType.TEXT,
+            information_id="test_id",
+            additional_meta={"test_key": "test_value"},
+            related_ids=["related_1", "related_2"],
+        )
+
+        assert isinstance(piece, InternalInformationPiece)
+        assert piece.type == ContentType.TEXT
+        assert piece.page_content == "Test content"
+        assert piece.metadata["document"] == "test_doc"
+        assert piece.metadata["page"] == 1
+        assert piece.metadata["title"] == "Test Title"
+        assert piece.metadata["id"] == "test_id"
+        assert piece.metadata["related"] == ["related_1", "related_2"]
+        assert piece.metadata["test_key"] == "test_value"
+
+    def test_is_text_based_threshold(self, pdf_extractor):
+        """Test the text-based page classification threshold."""
+        # Mock pdfplumber page
+        mock_page = MagicMock()
+
+        # Test case 1: Page with enough text (above threshold)
+        mock_page.extract_text.return_value = "A" * 60  # Above 50 character threshold
+        assert pdf_extractor._is_text_based(mock_page) is True
+
+        # Test case 2: Page with insufficient text (below threshold)
+        mock_page.extract_text.return_value = "A" * 30  # Below 50 character threshold
+        assert pdf_extractor._is_text_based(mock_page) is False
+
+        # Test case 3: Empty text
+        mock_page.extract_text.return_value = ""
+        assert pdf_extractor._is_text_based(mock_page) is False
+
+        # Test case 4: None text
+        mock_page.extract_text.return_value = None
+        assert pdf_extractor._is_text_based(mock_page) is False
+
+    def test_auto_detect_language(self, pdf_extractor):
+        """Test language detection functionality."""
+        # Test English text
+        english_text = "This is a sample English text for language detection."
+        detected_lang = pdf_extractor._auto_detect_language(english_text)
+        assert detected_lang == "en"  # Should detect a language
+        # Test German text
+        german_text = "Dies ist ein deutscher Beispiel Text für die Spracherkennung"
+        detected_lang = pdf_extractor._auto_detect_language(german_text)
+        assert detected_lang == "de"
+
+        # Test with empty text (should default to "en")
+        empty_text = ""
+        detected_lang = pdf_extractor._auto_detect_language(empty_text)
+        assert detected_lang == "en"
+
+    @pytest.mark.asyncio
+    async def test_extract_content_text_based_pdf(self, pdf_extractor, test_pdf_files):
+        """Test content extraction from text-based PDF."""
+        result = await pdf_extractor.aextract_content(
+            file_path=test_pdf_files["text_based"], name="text_based_document"
+        )
+
+        assert isinstance(result, list)
+        assert len(result) > 0
+
+        # Check that we have both text and table elements
+        text_elements = [elem for elem in result if elem.type == ContentType.TEXT]
+        table_elements = [elem for elem in result if elem.type == ContentType.TABLE]
+
+        assert len(text_elements) > 0, "Should extract at least one text element"
+        assert len(table_elements) > 0, "Should extract at least one table element"
+
+        # Verify metadata structure
+        for element in result:
+            assert "document" in element.metadata
+            assert "page" in element.metadata
+            assert "title" in element.metadata
+            assert "id" in element.metadata
+            assert "related" in element.metadata
+            assert element.metadata["document"] == "text_based_document"
+
+    @pytest.mark.asyncio
+    async def test_extract_content_scanned_pdf(self, pdf_extractor, test_pdf_files):
+        """Test content extraction from scanned PDF using OCR."""
+        result = await pdf_extractor.aextract_content(file_path=test_pdf_files["scanned"], name="scanned_document")
+
+        assert isinstance(result, list)
+
+        for element in result:
+            assert element.metadata["document"] == "scanned_document"
+            assert isinstance(element.metadata["page"], int)
+
+    @pytest.mark.asyncio
+    async def test_extract_content_mixed_content_pdf(self, pdf_extractor, test_pdf_files):
+        """Test content extraction from mixed content PDF."""
+        result = await pdf_extractor.aextract_content(
+            file_path=test_pdf_files["mixed_content"], name="mixed_content_document"
+        )
+
+        assert isinstance(result, list)
+        assert len(result) > 0
+
+        content_types = {elem.type for elem in result}
+        assert ContentType.TEXT in content_types
+        assert ContentType.TABLE in content_types
+
+        for element in result:
+            assert element.metadata["document"] == "mixed_content_document"
+
+    @pytest.mark.asyncio
+    async def test_extract_content_multi_column_pdf(self, pdf_extractor, test_pdf_files):
+        """Test content extraction from multi-column PDF."""
+        result = await pdf_extractor.aextract_content(
+            file_path=test_pdf_files["multi_column"], name="multi_column_document"
+        )
+
+        assert isinstance(result, list)
+        assert len(result) > 0
+
+        for element in result:
+            assert element.metadata["document"] == "multi_column_document"
+
+    def test_process_text_content_with_titles(self, pdf_extractor):
+        """Test text content processing with title detection."""
+        content = """1. Introduction
+This is the introduction section with substantial content.
+It contains multiple sentences and paragraphs.
+
+2. Methodology
+This section describes the methodology used in the research.
+It includes detailed explanations of procedures.
+
+3. State of the art
+This section describes the state of the art.
+
+3.1 Data Collection
+This subsection covers data collection procedures.
+"""
+
+        result = pdf_extractor._process_text_content(
+            content=content, title="Document Title", page_index=1, document_name="test_document"
+        )
+
+        assert isinstance(result, list)
+        assert len(result) == 4
+
+        # Check that titles are properly detected and content is split
+        content_found = [elem.page_content for elem in result]
+
+        # Should contain the processed content
+        assert any("1. Introduction" in content for content in content_found)
+        assert any("2. Methodology" in content for content in content_found)
+
+        for elem in result:
+            assert elem.metadata["title"] in [
+                "1. Introduction",
+                "2. Methodology",
+                "3. State of the art",
+                "3.1 Data Collection",
+            ]
+
+    def test_process_text_content_without_titles(self, pdf_extractor):
+        """Test text content processing without title patterns."""
+        content = "This is plain text content without any title patterns."
+
+        result = pdf_extractor._process_text_content(
+            content=content, title="Current Title", page_index=1, document_name="test_document"
+        )
+
+        assert isinstance(result, list)
+        assert len(result) == 1
+        assert result[0].metadata["title"] == "Current Title"
+        assert "Current Title" in result[0].page_content
+
+    def test_process_empty_text_content(self, pdf_extractor):
+        """Test processing empty or None text content."""
+        # Test empty string
+        result = pdf_extractor._process_text_content(
+            content="", title="Title", page_index=1, document_name="test_document"
+        )
+        assert result == []
+
+        # Test whitespace only
+        result = pdf_extractor._process_text_content(
+            content="   \n\t   ", title="Title", page_index=1, document_name="test_document"
+        )
+        assert result == []
+
+    # TODO: hier gehts weiter !!! D:
+
+    def test_extract_text_from_text_page(self, pdf_extractor):
+        """Test text extraction from text-based pages."""
+        mock_page = MagicMock()
+        mock_page.extract_text.return_value = "Sample extracted text"
+
+        result = pdf_extractor._extract_text_from_text_page(mock_page)
+        assert result == "Sample extracted text"
+
+        # Test error handling
+        mock_page.extract_text.side_effect = Exception("Extraction failed")
+        result = pdf_extractor._extract_text_from_text_page(mock_page)
+        assert result == ""
+
+    def test_extract_tables_from_text_page(self, pdf_extractor):
+        """Test table extraction from text-based pages."""
+        mock_page = MagicMock()
+
+        # Mock table data
+        mock_table = MagicMock()
+        mock_table.extract.return_value = [["Header 1", "Header 2"], ["Value 1", "Value 2"], ["Value 3", "Value 4"]]
+        mock_page.find_tables.return_value = [mock_table]
+
+        result = pdf_extractor._extract_tables_from_text_page(
+            page=mock_page, page_index=1, document_name="test_document"
+        )
+
+        assert isinstance(result, list)
+        assert len(result) > 0
+        assert result[0].type == ContentType.TABLE
+        assert result[0].metadata["document"] == "test_document"
+        assert result[0].metadata["page"] == 1
+
+    @pytest.mark.integration
+    def test_extract_text_from_scanned_page(self, pdf_extractor, test_pdf_files):
+        """Test text extraction from scanned pages using OCR with real PDF."""
+
+        # Use the actual scanned test PDF
+        scanned_pdf_path = test_pdf_files["scanned"]
+
+        # Open the real PDF and get the first page
+        with pdfplumber.open(scanned_pdf_path) as pdf:
+
+            page = pdf.pages[0]
+
+            # Convert PDF page to image for OCR
+            images = convert_from_path(scanned_pdf_path, first_page=1, last_page=1)
+
+            # Convert PIL image to numpy array
+            image_array = np.array(images[0])
+
+            # Test the actual OCR extraction
+            result = pdf_extractor._extract_text_from_scanned_page(
+                page=page, scale_x=1.0, scale_y=1.0, image=image_array, pdf_page_height=images[0].height
+            )
+
+            assert isinstance(result, tuple)
+            assert len(result) == 2
+            text, pdf_bytes = result
+
+            assert isinstance(text, str)
+            assert isinstance(pdf_bytes, bytes)
+
+            # The OCR should extract some text (even if not perfect)
+            assert len(text.strip()) > 0, "OCR should extract some text from scanned page"
+
+            # PDF bytes should be generated
+            assert len(pdf_bytes) > 0, "Should generate PDF bytes from OCR"
+
+            # Verify specific content from the scanned PDF
+            expected_content = [
+                "SCANNED DOCUMENT",
+                "This document appears to be scanned from a physical paper",
+                "text extraction capabilities are limited",
+                "Key characteristics of scanned documents",
+                "OCR is required for text extraction",
+                "testing purposes",
+            ]
+
+            # Check that the main content elements are extracted
+            text_upper = text.upper()
+            for expected_phrase in expected_content:
+                assert (
+                    expected_phrase.upper() in text_upper
+                ), f"Expected phrase '{expected_phrase}' not found in OCR text"
+
+            with patch.object(pdf_extractor, "_auto_detect_language", return_value="de"):
+                result = pdf_extractor._extract_text_from_scanned_page(
+                    page=page, scale_x=1.0, scale_y=1.0, image=image_array, pdf_page_height=images[0].height
+                )
+
+                assert isinstance(result, tuple)
+                assert len(result) == 2
+                text, pdf_bytes = result
+                assert isinstance(text, str)
+                assert isinstance(pdf_bytes, bytes)
+
+    @patch("camelot.read_pdf")
+    def test_extract_tables_from_scanned_page(self, mock_camelot, pdf_extractor):
+        """Test table extraction from scanned pages."""
+        # Mock Camelot table extraction
+        mock_camelot_table = MagicMock()
+        mock_camelot_table.accuracy = 75
+        mock_camelot_table.df = pd.DataFrame({"Column 1": ["Value 1", "Value 2"], "Column 2": ["Value 3", "Value 4"]})
+        mock_camelot.return_value = [mock_camelot_table]
+
+        result = pdf_extractor._extract_tables_from_scanned_page(
+            page_index=1, document_name="test_document", filename="/path/to/test.pdf"
+        )
+
+        assert isinstance(result, list)
+        if len(result) > 0:  # Camelot succeeded
+            assert result[0].type == ContentType.TABLE
+            assert result[0].metadata["table_method"] == "camelot"
+            assert result[0].metadata["accuracy"] == 75
+
+    def test_title_pattern_detection(self, pdf_extractor):
+        """Test title pattern regular expressions with precise assertions."""
+        # Test single line titles with TITLE_PATTERN.search()
+        test_cases = [
+            "1. Introduction",
+            "2.1 Methodology",
+            "3.1.1 Data Collection",
+            "4.\tResults",
+            "5. Discussion and Conclusions",
+            "1.2.3.4.5 Many dots",
+        ]
+
+        for test_case in test_cases:
+            match = pdf_extractor.TITLE_PATTERN.search(test_case)
+            assert match is not None, f"TITLE_PATTERN should match: '{test_case}'"
+
+            assert match.group(1) == "", f"Group 1 should be empty for start-of-string match, got: '{match.group(1)}'"
+
+            extracted_title = match.group(2)
+            assert extracted_title == test_case, f"Expected title '{test_case}', but extracted '{extracted_title}'"
+
+        # Test titles that should NOT match (edge cases)
+        non_matching_cases = [
+            "Introduction without number",
+            "A. Section with letter",
+            "1.",  # No text after number
+            "1 Missing dot",
+        ]
+
+        for non_match_case in non_matching_cases:
+            match = pdf_extractor.TITLE_PATTERN.search(non_match_case)
+            assert match is None, f"TITLE_PATTERN should NOT match: '{non_match_case}'"
+
+        # Test multiline title detection with TITLE_PATTERN_MULTILINE.findall()
+        multiline_text = """Some content here.
+
+1. First Section
+This is the content of the first section.
+
+2. Second Section
+This is the content of the second section.
+
+3.1 Subsection Example
+More content here."""
+
+        matches = pdf_extractor.TITLE_PATTERN_MULTILINE.findall(multiline_text)
+        expected_titles = ["1. First Section", "2. Second Section", "3.1 Subsection Example"]
+
+        # Verify we found the expected number of matches
+        assert len(matches) == 3, f"Should find 3 title patterns, found {len(matches)}: {matches}"
+
+        # Each match is a tuple: (line_start_group, title_text_group)
+        # Extract just the title text (group 2) from each match tuple
+        found_titles = [match[1] for match in matches]
+
+        # Compare the exact titles found vs expected
+        assert found_titles == expected_titles, f"Expected titles {expected_titles}, but found {found_titles}"
+
+        # Verify that group 1 contains newlines for multiline matches
+        line_starts = [match[0] for match in matches]
+        expected_line_starts = ["\n", "\n", "\n"]  # All should have newline since they're after content
+        assert (
+            line_starts == expected_line_starts
+        ), f"Expected line starts {expected_line_starts}, but found {line_starts}"
+
+        # Test edge case: title at very beginning of text
+        text_with_title_at_start = "1. Starting Title\nSome content follows."
+        start_matches = pdf_extractor.TITLE_PATTERN_MULTILINE.findall(text_with_title_at_start)
+        assert len(start_matches) == 1, f"Should find 1 title at start, found: {start_matches}"
+        assert start_matches[0][0] == "", f"Group 1 should be empty for start-of-text, got: '{start_matches[0][0]}'"
+        assert start_matches[0][1] == "1. Starting Title", f"Should extract correct title, got: '{start_matches[0][1]}'"
+
+    @pytest.mark.asyncio
+    async def test_error_handling_invalid_file(self, pdf_extractor):
+        """Test error handling with invalid PDF file."""
+        invalid_path = Path("/nonexistent/file.pdf")
+
+        with pytest.raises(PDFPageCountError):
+            await pdf_extractor.aextract_content(file_path=invalid_path, name="invalid_document")
+
+    @pytest.mark.asyncio
+    async def test_related_ids_mapping(self, pdf_extractor, test_pdf_files):
+        """Test that related IDs are properly set between text and table elements using actual extractor."""
+        # Use the actual PDF extractor with a real test file
+        for test_file in test_pdf_files.values():
+
+            # Extract content using the actual extractor
+            result = await pdf_extractor.aextract_content(file_path=test_file, name="test_document")
+
+            # Filter text and table elements
+            text_elements = [elem for elem in result if elem.type == ContentType.TEXT]
+            table_elements = [elem for elem in result if elem.type == ContentType.TABLE]
+
+            # Test only if we have both text and table elements from the same page
+            if len(text_elements) > 0 and len(table_elements) > 0:
+                # Check that text elements have table IDs in their related field
+                text_element = text_elements[0]
+                table_element = table_elements[0]
+
+                # If elements are from the same page, they should reference each other
+                same_page_text = [
+                    elem for elem in text_elements if elem.metadata["page"] == table_element.metadata["page"]
+                ]
+                same_page_tables = [
+                    elem for elem in table_elements if elem.metadata["page"] == text_element.metadata["page"]
+                ]
+
+                if same_page_text and same_page_tables:
+                    # Get IDs from same page elements
+                    same_page_text_ids = [elem.metadata["id"] for elem in same_page_text]
+                    same_page_table_ids = [elem.metadata["id"] for elem in same_page_tables]
+
+                    # Verify that table elements reference text elements from the same page
+                    for table_elem in same_page_tables:
+                        assert "related" in table_elem.metadata
+                        related_ids = table_elem.metadata["related"]
+                        # The related IDs should contain text element IDs from the same page
+                        for text_id in same_page_text_ids:
+                            assert text_id in related_ids, f"Table element should reference text element {text_id}"
+
+                    # Verify that text elements reference table elements from the same page
+                    for text_elem in same_page_text:
+                        assert "related" in text_elem.metadata
+                        related_ids = text_elem.metadata["related"]
+                        # The related IDs should contain table element IDs from the same page
+                        for table_id in same_page_table_ids:
+                            assert table_id in related_ids, f"Text element should reference table element {table_id}"
+
+            # Verify all elements have the related field (even if empty)
+            for element in result:
+                assert "related" in element.metadata, "All elements should have 'related' field in metadata"
+                assert isinstance(element.metadata["related"], list), "'related' field should be a list"
+
+    @pytest.mark.asyncio
+    async def test_performance_with_large_pdf(self, pdf_extractor, test_pdf_files):
+        """Test performance with larger PDF files."""
+        # Use one of the existing test files
+        test_file = None
+        for file_path in test_pdf_files.values():
+            if file_path.exists():
+                test_file = file_path
+                break
+
+        start_time = time.time()
+
+        result = await pdf_extractor.aextract_content(file_path=test_file, name="performance_test")
+
+        end_time = time.time()
+        processing_time = end_time - start_time
+
+        assert isinstance(result, list)
+        # Ensure processing doesn't take too long (adjust threshold as needed)
+        assert processing_time < 60, f"Processing took too long: {processing_time} seconds"
+
+    def test_language_mapping(self, pdf_extractor):
+        """Test language code mapping for OCR."""
+        assert pdf_extractor._lang_map["en"] == "eng"
+        assert pdf_extractor._lang_map["de"] == "deu"
+
+        # Test fallback for unknown language
+        unknown_lang = "unknown"
+        tesseract_lang = pdf_extractor._lang_map.get(unknown_lang, "eng")
+        assert tesseract_lang == "eng"
+
+    @pytest.mark.integration
+    @pytest.mark.asyncio
+    async def test_end_to_end_extraction(self, pdf_extractor, test_pdf_files):
+        """Integration test for complete PDF extraction workflow."""
+        for pdf_name, pdf_path in test_pdf_files.items():
+            if not pdf_path.exists():
+                continue
+
+            result = await pdf_extractor.aextract_content(file_path=pdf_path, name=pdf_name)
+
+            assert isinstance(result, list), f"Result should be a list for {pdf_name}"
+            # Analyze results
+            text_count = sum(1 for elem in result if elem.type == ContentType.TEXT)
+            table_count = sum(1 for elem in result if elem.type == ContentType.TABLE)
+
+            logger.info(f"  Text elements: {text_count}")
+            logger.info(f"  Table elements: {table_count}")
+
+            # Verify metadata completeness
+            for i, element in enumerate(result):
+                assert "document" in element.metadata, f"Missing document in element {i}"
+                assert "page" in element.metadata, f"Missing page in element {i}"
+                assert "id" in element.metadata, f"Missing id in element {i}"
+                assert element.metadata["document"] == pdf_name, f"Wrong document name in element {i}"
+
+            # Verify content is not empty
+            non_empty_content = [elem for elem in result if elem.page_content.strip()]
+            assert len(non_empty_content) > 0, f"No non-empty content extracted from {pdf_name}"
diff --git a/extractor-api-lib/tests/test_data/generate_test_pdfs.py b/extractor-api-lib/tests/test_data/generate_test_pdfs.py
new file mode 100644
index 0000000..cebbfe9
--- /dev/null
+++ b/extractor-api-lib/tests/test_data/generate_test_pdfs.py
@@ -0,0 +1,876 @@
+#!/usr/bin/env python3
+"""Script to generate test PDF files for PDF extractor testing."""
+
+import io
+from pathlib import Path
+from PIL import Image, ImageDraw, ImageFont
+import matplotlib.pyplot as plt
+import numpy as np
+from reportlab.lib import colors
+from reportlab.lib.pagesizes import letter, A4
+from reportlab.platypus import (
+    SimpleDocTemplate,
+    Paragraph,
+    Spacer,
+    Table,
+    TableStyle,
+    Image as RLImage,
+    PageBreak,
+    KeepTogether,
+    BaseDocTemplate,
+    PageTemplate,
+    Frame,
+)
+from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
+from reportlab.lib.units import inch, cm
+from reportlab.platypus.flowables import HRFlowable
+import tempfile
+
+
+def create_test_data_dir():
+    """Create the test data directory if it doesn't exist."""
+    test_data_dir = Path(__file__).parent
+    test_data_dir.mkdir(exist_ok=True)
+    return test_data_dir
+
+
+def generate_text_based_pdf():
+    """Generate a PDF with substantial text content and comprehensive tables."""
+    test_data_dir = create_test_data_dir()
+    filename = test_data_dir / "text_based_document.pdf"
+
+    doc = SimpleDocTemplate(
+        str(filename),
+        pagesize=letter,
+        topMargin=0.75 * inch,
+        bottomMargin=0.75 * inch,
+        leftMargin=0.75 * inch,
+        rightMargin=0.75 * inch,
+    )
+    styles = getSampleStyleSheet()
+    story = []
+
+    # Title
+    title_style = ParagraphStyle(
+        "CustomTitle",
+        parent=styles["Heading1"],
+        fontSize=24,
+        spaceAfter=30,
+        alignment=1,  # Center alignment
+        textColor=colors.darkblue,
+    )
+    story.append(Paragraph("Advanced Text-Based Document for Testing", title_style))
+    story.append(Spacer(1, 20))
+
+    # Section 1: Introduction
+    story.append(Paragraph("1. Introduction", styles["Heading2"]))
+    intro_text = """
+    This is a comprehensive introduction section that contains substantial text content.
+    The purpose of this document is to test the PDF extractor's ability to correctly
+    classify pages as text-based when they contain primarily extractable text content.
+    This section has been designed to exceed the 50-character threshold used by the
+    classification algorithm to ensure proper categorization as a TEXT_BASED page type.
+    The document includes multiple tables with different structures to test table extraction capabilities,
+    including nested headers, merged cells, and complex formatting scenarios.
+    """
+    story.append(Paragraph(intro_text, styles["Normal"]))
+    story.append(Spacer(1, 12))
+
+    # Section 2: Methodology with enhanced tables
+    story.append(Paragraph("2. Methodology", styles["Heading2"]))
+    method_text = """
+    This section describes the methodology used in our research. The approach involves
+    multiple phases of data collection, analysis, and validation. Each phase is carefully
+    designed to ensure accuracy and reliability of the results obtained.
+    """
+    story.append(Paragraph(method_text, styles["Normal"]))
+    story.append(Spacer(1, 12))
+
+    # Enhanced Table 1: Project Phases with merged cells
+    story.append(Paragraph("2.1 Project Phases Overview", styles["Heading3"]))
+    table1_data = [
+        ["Phase", "Description", "Timeline", "Resources Required", "Expected Deliverables"],
+        [
+            "Planning",
+            "Initial project setup and requirement analysis",
+            "2 weeks",
+            "1 PM, 2 analysts",
+            "Project charter, requirements doc",
+        ],
+        [
+            "Design",
+            "System architecture and detailed design",
+            "3 weeks",
+            "2 architects, 1 designer",
+            "Technical specifications",
+        ],
+        ["Development", "Implementation of core functionality", "8 weeks", "4 developers, 1 lead", "Working prototype"],
+        ["Testing", "Quality assurance and validation", "3 weeks", "3 testers, 1 QA lead", "Test reports, bug fixes"],
+        ["Deployment", "Production deployment and monitoring", "1 week", "2 DevOps, 1 admin", "Live system"],
+        ["Maintenance", "Ongoing support and updates", "Ongoing", "1 support engineer", "Monthly reports"],
+    ]
+
+    table1 = Table(table1_data, colWidths=[1 * inch, 2 * inch, 1 * inch, 1.5 * inch, 1.5 * inch])
+    table1.setStyle(
+        TableStyle(
+            [
+                ("BACKGROUND", (0, 0), (-1, 0), colors.darkblue),
+                ("TEXTCOLOR", (0, 0), (-1, 0), colors.whitesmoke),
+                ("ALIGN", (0, 0), (-1, -1), "LEFT"),
+                ("VALIGN", (0, 0), (-1, -1), "MIDDLE"),
+                ("FONTNAME", (0, 0), (-1, 0), "Helvetica-Bold"),
+                ("FONTSIZE", (0, 0), (-1, 0), 10),
+                ("BOTTOMPADDING", (0, 0), (-1, 0), 12),
+                ("TOPPADDING", (0, 0), (-1, 0), 12),
+                ("BACKGROUND", (0, 1), (-1, -1), colors.lightblue),
+                ("ALTERNATEROWCOLORS", (0, 1), (-1, -1), [colors.lightblue, colors.white]),
+                ("GRID", (0, 0), (-1, -1), 1, colors.black),
+                ("FONTSIZE", (0, 1), (-1, -1), 9),
+                ("LEFTPADDING", (0, 0), (-1, -1), 6),
+                ("RIGHTPADDING", (0, 0), (-1, -1), 6),
+            ]
+        )
+    )
+
+    story.append(KeepTogether(table1))
+    story.append(Spacer(1, 20))
+
+    # Table 2: Detailed Budget Breakdown with subtotals
+    story.append(Paragraph("2.2 Comprehensive Budget Analysis", styles["Heading3"]))
+    table2_data = [
+        ["Category", "Item", "Q1 Budget", "Q1 Actual", "Q2 Budget", "Q2 Actual", "Total Variance"],
+        ["Personnel", "Senior Developers", "$35,000", "$34,200", "$36,000", "$35,800", "-$1,000"],
+        ["Personnel", "Junior Developers", "$15,000", "$14,300", "$16,000", "$15,400", "-$1,300"],
+        ["Personnel", "Project Manager", "$12,000", "$12,000", "$12,500", "$12,500", "$0"],
+        ["Personnel", "QA Engineers", "$8,000", "$7,800", "$8,500", "$8,200", "-$500"],
+        ["", "Personnel Subtotal", "$70,000", "$68,300", "$73,000", "$71,900", "-$2,800"],
+        ["Equipment", "Hardware", "$15,000", "$16,200", "$12,000", "$11,800", "-$1,000"],
+        ["Equipment", "Software Licenses", "$8,000", "$7,900", "$8,500", "$8,300", "-$300"],
+        ["Equipment", "Cloud Services", "$5,000", "$4,800", "$5,500", "$5,400", "-$300"],
+        ["", "Equipment Subtotal", "$28,000", "$28,900", "$26,000", "$25,500", "-$1,600"],
+        ["Operations", "Office Rent", "$6,000", "$6,000", "$6,200", "$6,200", "$0"],
+        ["Operations", "Utilities", "$2,000", "$1,950", "$2,100", "$2,050", "-$100"],
+        ["Operations", "Internet/Phone", "$1,500", "$1,450", "$1,600", "$1,550", "-$100"],
+        ["", "Operations Subtotal", "$9,500", "$9,400", "$9,900", "$9,800", "-$200"],
+        ["", "GRAND TOTAL", "$107,500", "$106,600", "$108,900", "$107,200", "-$4,600"],
+    ]
+
+    table2 = Table(
+        table2_data, colWidths=[1 * inch, 1.2 * inch, 0.9 * inch, 0.9 * inch, 0.9 * inch, 0.9 * inch, 1 * inch]
+    )
+    table2.setStyle(
+        TableStyle(
+            [
+                ("BACKGROUND", (0, 0), (-1, 0), colors.darkgreen),
+                ("TEXTCOLOR", (0, 0), (-1, 0), colors.whitesmoke),
+                ("ALIGN", (0, 0), (-1, -1), "CENTER"),
+                ("VALIGN", (0, 0), (-1, -1), "MIDDLE"),
+                ("FONTNAME", (0, 0), (-1, 0), "Helvetica-Bold"),
+                ("FONTSIZE", (0, 0), (-1, 0), 9),
+                ("BOTTOMPADDING", (0, 0), (-1, 0), 8),
+                ("TOPPADDING", (0, 0), (-1, 0), 8),
+                ("BACKGROUND", (0, 1), (-1, -1), colors.lightgreen),
+                # Subtotal rows formatting
+                ("BACKGROUND", (0, 5), (-1, 5), colors.yellow),  # Personnel subtotal
+                ("BACKGROUND", (0, 9), (-1, 9), colors.yellow),  # Equipment subtotal
+                ("BACKGROUND", (0, 13), (-1, 13), colors.yellow),  # Operations subtotal
+                ("BACKGROUND", (0, -1), (-1, -1), colors.orange),  # Grand total
+                ("FONTNAME", (0, 5), (-1, 5), "Helvetica-Bold"),
+                ("FONTNAME", (0, 9), (-1, 9), "Helvetica-Bold"),
+                ("FONTNAME", (0, 13), (-1, 13), "Helvetica-Bold"),
+                ("FONTNAME", (0, -1), (-1, -1), "Helvetica-Bold"),
+                ("GRID", (0, 0), (-1, -1), 1, colors.black),
+                ("FONTSIZE", (0, 1), (-1, -1), 8),
+                ("LEFTPADDING", (0, 0), (-1, -1), 4),
+                ("RIGHTPADDING", (0, 0), (-1, -1), 4),
+            ]
+        )
+    )
+
+    story.append(KeepTogether(table2))
+    story.append(Spacer(1, 12))
+
+    # Page break for next section
+    story.append(PageBreak())
+
+    # Section 3: Results with complex data table
+    story.append(Paragraph("3. Detailed Results and Performance Analysis", styles["Heading2"]))
+    results_text = """
+    The results obtained from our methodology show significant improvements in accuracy
+    and efficiency across multiple metrics. The data processing phase revealed interesting 
+    patterns that were not immediately apparent in the raw data. Statistical analysis 
+    confirmed the validity of our hypotheses with a confidence level of 95%.
+    """
+    story.append(Paragraph(results_text, styles["Normal"]))
+    story.append(Spacer(1, 12))
+
+    # Table 3: Complex Performance Metrics with statistical data
+    story.append(Paragraph("3.1 Comprehensive Performance Metrics", styles["Heading3"]))
+    table3_data = [
+        ["Metric", "Unit", "Baseline", "Method A", "Method B", "Method C", "Best Result", "Improvement"],
+        ["Accuracy", "%", "78.5 ± 2.1", "85.2 ± 1.8", "89.1 ± 1.5", "92.3 ± 1.2", "92.3", "+17.6%"],
+        ["Precision", "%", "75.2 ± 2.5", "82.8 ± 2.0", "87.5 ± 1.7", "90.1 ± 1.4", "90.1", "+19.8%"],
+        ["Recall", "%", "80.1 ± 2.3", "88.3 ± 1.9", "91.2 ± 1.6", "93.5 ± 1.3", "93.5", "+16.7%"],
+        ["F1-Score", "%", "77.5 ± 2.2", "85.4 ± 1.8", "89.3 ± 1.5", "91.7 ± 1.3", "91.7", "+18.3%"],
+        ["Processing Time", "ms", "245 ± 15", "189 ± 12", "156 ± 10", "134 ± 8", "134", "-45.3%"],
+        ["Memory Usage", "MB", "128 ± 8", "98 ± 6", "87 ± 5", "76 ± 4", "76", "-40.6%"],
+        ["Throughput", "req/s", "120 ± 10", "165 ± 12", "210 ± 15", "245 ± 18", "245", "+104.2%"],
+        ["Error Rate", "%", "4.2 ± 0.8", "2.8 ± 0.6", "1.9 ± 0.4", "1.1 ± 0.3", "1.1", "-73.8%"],
+    ]
+
+    table3 = Table(
+        table3_data, colWidths=[1 * inch, 0.6 * inch, 1 * inch, 1 * inch, 1 * inch, 1 * inch, 0.8 * inch, 0.8 * inch]
+    )
+    table3.setStyle(
+        TableStyle(
+            [
+                ("BACKGROUND", (0, 0), (-1, 0), colors.purple),
+                ("TEXTCOLOR", (0, 0), (-1, 0), colors.whitesmoke),
+                ("ALIGN", (0, 0), (-1, -1), "CENTER"),
+                ("VALIGN", (0, 0), (-1, -1), "MIDDLE"),
+                ("FONTNAME", (0, 0), (-1, 0), "Helvetica-Bold"),
+                ("FONTSIZE", (0, 0), (-1, 0), 9),
+                ("BOTTOMPADDING", (0, 0), (-1, 0), 10),
+                ("TOPPADDING", (0, 0), (-1, 0), 10),
+                ("BACKGROUND", (0, 1), (-1, -1), colors.lavender),
+                ("BACKGROUND", (-2, 1), (-2, -1), colors.lightgreen),  # Best result column
+                ("BACKGROUND", (-1, 1), (-1, -1), colors.lightyellow),  # Improvement column
+                ("GRID", (0, 0), (-1, -1), 1, colors.black),
+                ("FONTSIZE", (0, 1), (-1, -1), 8),
+                ("LEFTPADDING", (0, 0), (-1, -1), 4),
+                ("RIGHTPADDING", (0, 0), (-1, -1), 4),
+            ]
+        )
+    )
+
+    story.append(KeepTogether(table3))
+    story.append(Spacer(1, 20))
+
+    # Table 4: Cross-validation results
+    story.append(Paragraph("3.2 Cross-Validation Results", styles["Heading3"]))
+    table4_data = [
+        ["Fold", "Training Acc", "Validation Acc", "Test Acc", "Precision", "Recall", "F1-Score"],
+        ["Fold 1", "94.2%", "91.8%", "90.5%", "89.7%", "91.2%", "90.4%"],
+        ["Fold 2", "93.8%", "92.1%", "91.0%", "90.3%", "91.8%", "91.0%"],
+        ["Fold 3", "94.5%", "91.5%", "90.8%", "90.1%", "91.5%", "90.8%"],
+        ["Fold 4", "94.1%", "92.3%", "91.5%", "91.0%", "92.1%", "91.5%"],
+        ["Fold 5", "93.9%", "91.9%", "90.9%", "90.5%", "91.4%", "90.9%"],
+        ["Mean", "94.1%", "91.9%", "90.9%", "90.3%", "91.6%", "90.9%"],
+        ["Std Dev", "0.28%", "0.31%", "0.37%", "0.48%", "0.35%", "0.41%"],
+    ]
+
+    table4 = Table(table4_data)
+    table4.setStyle(
+        TableStyle(
+            [
+                ("BACKGROUND", (0, 0), (-1, 0), colors.darkred),
+                ("TEXTCOLOR", (0, 0), (-1, 0), colors.whitesmoke),
+                ("ALIGN", (0, 0), (-1, -1), "CENTER"),
+                ("VALIGN", (0, 0), (-1, -1), "MIDDLE"),
+                ("FONTNAME", (0, 0), (-1, 0), "Helvetica-Bold"),
+                ("FONTSIZE", (0, 0), (-1, 0), 10),
+                ("BOTTOMPADDING", (0, 0), (-1, 0), 12),
+                ("TOPPADDING", (0, 0), (-1, 0), 12),
+                ("BACKGROUND", (0, 1), (-1, -3), colors.mistyrose),
+                ("BACKGROUND", (0, -2), (-1, -2), colors.lightcyan),  # Mean row
+                ("BACKGROUND", (0, -1), (-1, -1), colors.lightgrey),  # Std dev row
+                ("FONTNAME", (0, -2), (-1, -1), "Helvetica-Bold"),
+                ("GRID", (0, 0), (-1, -1), 1, colors.black),
+                ("FONTSIZE", (0, 1), (-1, -1), 9),
+            ]
+        )
+    )
+
+    story.append(KeepTogether(table4))
+    story.append(Spacer(1, 12))
+
+    # Section 4: Conclusion
+    story.append(Paragraph("4. Comprehensive Conclusion", styles["Heading2"]))
+    conclusion_text = """
+    In conclusion, this study demonstrates the effectiveness of the proposed methodology
+    across multiple evaluation criteria. The results provide strong evidence supporting 
+    our initial hypotheses and open new avenues for future research. The implications 
+    of these findings extend beyond the immediate scope of this study and may have 
+    broader applications in the field. The comprehensive tables above clearly show 
+    the progression and improvements achieved through our systematic approach, with 
+    statistical significance confirmed through rigorous cross-validation procedures.
+    
+    The enhanced table structures in this document serve as comprehensive test cases
+    for PDF extraction algorithms, including scenarios with merged cells, nested headers,
+    subtotals, and complex formatting requirements.
+    """
+    story.append(Paragraph(conclusion_text, styles["Normal"]))
+
+    doc.build(story)
+    print(f"Generated enhanced text-based PDF with advanced tables: {filename}")
+    return filename
+
+
+def generate_scanned_image_pdf():
+    """Generate a PDF that simulates a scanned document with minimal extractable text."""
+    test_data_dir = create_test_data_dir()
+    filename = test_data_dir / "scanned_document.pdf"
+
+    # Create an image that looks like a scanned document
+    img_width, img_height = 595, 842  # A4 size in points
+    image = Image.new("RGB", (img_width, img_height), color="white")
+    draw = ImageDraw.Draw(image)
+
+    # Try to use a default font, fall back to default if not available
+    try:
+        font_large = ImageFont.truetype("Arial.ttf", 20)
+        font_medium = ImageFont.truetype("Arial.ttf", 16)
+        font_small = ImageFont.truetype("Arial.ttf", 12)
+    except OSError:
+        try:
+            font_large = ImageFont.truetype("/System/Library/Fonts/Arial.ttf", 20)
+            font_medium = ImageFont.truetype("/System/Library/Fonts/Arial.ttf", 16)
+            font_small = ImageFont.truetype("/System/Library/Fonts/Arial.ttf", 12)
+        except OSError:
+            font_large = ImageFont.load_default()
+            font_medium = ImageFont.load_default()
+            font_small = ImageFont.load_default()
+
+    # Add title
+    draw.text((50, 50), "SCANNED DOCUMENT", fill="black", font=font_large)
+
+    # Add some body text to simulate scanned content
+    y_pos = 100
+    lines = [
+        "This document appears to be scanned from a physical paper.",
+        "The text extraction capabilities are limited because",
+        "the content exists as image data rather than searchable text.",
+        "",
+        "Key characteristics of scanned documents:",
+        "• Text is part of the image",
+        "• OCR is required for text extraction",
+        "• Quality depends on scan resolution",
+        "• May contain artifacts and noise",
+        "",
+        "This simulation creates an image-based PDF that mimics",
+        "the behavior of a real scanned document for testing purposes.",
+    ]
+
+    for line in lines:
+        draw.text((50, y_pos), line, fill="black", font=font_small)
+        y_pos += 25
+
+    # Add some visual noise to simulate scan artifacts
+    for i in range(100):
+        x = hash(f"noise_x_{i}") % img_width
+        y = hash(f"noise_y_{i}") % img_height
+        draw.point((x, y), fill="lightgray")
+
+    # Save image to temporary file and create PDF
+    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_img:
+        image.save(temp_img.name, "PNG")
+
+        # Create PDF with the image
+        doc = SimpleDocTemplate(str(filename), pagesize=letter)
+        story = []
+
+        # Add the image to PDF
+        rl_image = RLImage(temp_img.name, width=6 * inch, height=8 * inch)
+        story.append(rl_image)
+
+        doc.build(story)
+
+    # Clean up temporary file
+    Path(temp_img.name).unlink()
+
+    print(f"Generated scanned PDF: {filename}")
+    return filename
+
+
+def generate_mixed_content_pdf():
+    """Generate a PDF with both extractable text and embedded images."""
+    test_data_dir = create_test_data_dir()
+    filename = test_data_dir / "mixed_content_document.pdf"
+
+    doc = SimpleDocTemplate(
+        str(filename),
+        pagesize=letter,
+        topMargin=0.75 * inch,
+        bottomMargin=0.75 * inch,
+        leftMargin=0.75 * inch,
+        rightMargin=0.75 * inch,
+    )
+    styles = getSampleStyleSheet()
+    story = []
+
+    # Title
+    title_style = ParagraphStyle(
+        "CustomTitle", parent=styles["Heading1"], fontSize=24, spaceAfter=30, alignment=1, textColor=colors.darkred
+    )
+    story.append(Paragraph("Advanced Mixed Content Document for Testing", title_style))
+    story.append(Spacer(1, 20))
+
+    # Section with text
+    story.append(Paragraph("1. Overview", styles["Heading2"]))
+    overview_text = """
+    This document contains mixed content including both extractable text and embedded images.
+    The purpose is to test the PDF extractor's ability to correctly classify pages as MIXED
+    when they contain substantial text content along with image elements. This classification
+    is important for determining the appropriate extraction strategy to use. The document
+    includes multiple visualizations, charts, and complex layouts to thoroughly test
+    image-text relationship extraction capabilities.
+    """
+    story.append(Paragraph(overview_text, styles["Normal"]))
+    story.append(Spacer(1, 12))
+
+    # Create multiple chart images using matplotlib
+
+    # Chart 1: Bar chart
+    fig1, ax1 = plt.subplots(figsize=(6, 4))
+    data1 = [23, 45, 56, 78, 32]
+    labels1 = ["Q1", "Q2", "Q3", "Q4", "Q5"]
+    bars = ax1.bar(labels1, data1, color=["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd"])
+    ax1.set_title("Quarterly Performance Data", fontsize=14, fontweight="bold")
+    ax1.set_ylabel("Performance Score", fontsize=12)
+    ax1.set_xlabel("Quarter", fontsize=12)
+
+    # Add value labels on bars
+    for bar in bars:
+        height = bar.get_height()
+        ax1.text(
+            bar.get_x() + bar.get_width() / 2.0, height + 1, f"{height}", ha="center", va="bottom", fontweight="bold"
+        )
+
+    ax1.grid(axis="y", alpha=0.3)
+    plt.tight_layout()
+
+    # Save chart to temporary file
+    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_chart1:
+        fig1.savefig(temp_chart1.name, dpi=150, bbox_inches="tight")
+        plt.close(fig1)
+
+        # Add the chart to PDF
+        chart_image1 = RLImage(temp_chart1.name, width=5 * inch, height=3.5 * inch)
+        story.append(chart_image1)
+        story.append(Spacer(1, 12))
+
+    # Text analysis section
+    story.append(Paragraph("2. Performance Analysis", styles["Heading2"]))
+    analysis_text = """
+    The chart above illustrates the quarterly performance trends observed during our study period.
+    As evident from the data visualization, there is a clear upward trend from Q1 to Q4, followed
+    by a decline in Q5. This pattern suggests seasonal variations that should be considered in
+    future planning and resource allocation decisions. The performance metrics show significant
+    improvement during the middle quarters, with Q4 representing peak performance.
+    """
+    story.append(Paragraph(analysis_text, styles["Normal"]))
+    story.append(Spacer(1, 12))
+
+    # Enhanced table with data
+    table_data = [
+        ["Quarter", "Score", "Change %", "Status", "Target", "Variance"],
+        ["Q1", "23", "-", "Baseline", "25", "-8.0%"],
+        ["Q2", "45", "+95.7%", "Improving", "40", "+12.5%"],
+        ["Q3", "56", "+24.4%", "Good", "55", "+1.8%"],
+        ["Q4", "78", "+39.3%", "Excellent", "70", "+11.4%"],
+        ["Q5", "32", "-59.0%", "Concerning", "75", "-57.3%"],
+    ]
+
+    table = Table(table_data, colWidths=[0.9 * inch, 0.7 * inch, 0.9 * inch, 1 * inch, 0.7 * inch, 0.9 * inch])
+    table.setStyle(
+        TableStyle(
+            [
+                ("BACKGROUND", (0, 0), (-1, 0), colors.darkblue),
+                ("TEXTCOLOR", (0, 0), (-1, 0), colors.whitesmoke),
+                ("ALIGN", (0, 0), (-1, -1), "CENTER"),
+                ("VALIGN", (0, 0), (-1, -1), "MIDDLE"),
+                ("FONTNAME", (0, 0), (-1, 0), "Helvetica-Bold"),
+                ("FONTSIZE", (0, 0), (-1, 0), 10),
+                ("BOTTOMPADDING", (0, 0), (-1, 0), 12),
+                ("TOPPADDING", (0, 0), (-1, 0), 12),
+                ("BACKGROUND", (0, 1), (-1, -1), colors.lightblue),
+                # Color code status column
+                ("BACKGROUND", (3, 2), (3, 2), colors.yellow),  # Improving
+                ("BACKGROUND", (3, 3), (3, 3), colors.lightgreen),  # Good
+                ("BACKGROUND", (3, 4), (3, 4), colors.green),  # Excellent
+                ("BACKGROUND", (3, 5), (3, 5), colors.salmon),  # Concerning
+                ("GRID", (0, 0), (-1, -1), 1, colors.black),
+                ("FONTSIZE", (0, 1), (-1, -1), 9),
+            ]
+        )
+    )
+
+    story.append(table)
+    story.append(Spacer(1, 15))
+
+    # Chart 2: Line plot with trend analysis
+    fig2, ax2 = plt.subplots(figsize=(7, 4))
+
+    # Multiple data series
+    quarters = ["Q1", "Q2", "Q3", "Q4", "Q5"]
+    actual = [23, 45, 56, 78, 32]
+    target = [25, 40, 55, 70, 75]
+    forecast = [20, 42, 58, 75, 68]
+
+    ax2.plot(quarters, actual, marker="o", linewidth=2, label="Actual", color="#d62728")
+    ax2.plot(quarters, target, marker="s", linewidth=2, label="Target", color="#2ca02c", linestyle="--")
+    ax2.plot(quarters, forecast, marker="^", linewidth=2, label="Forecast", color="#ff7f0e", linestyle=":")
+
+    ax2.set_title("Performance Trends: Actual vs Target vs Forecast", fontsize=14, fontweight="bold")
+    ax2.set_ylabel("Performance Score", fontsize=12)
+    ax2.set_xlabel("Quarter", fontsize=12)
+    ax2.legend(loc="upper left")
+    ax2.grid(True, alpha=0.3)
+
+    # Add annotations
+    ax2.annotate(
+        "Peak Performance",
+        xy=("Q4", 78),
+        xytext=("Q3", 85),
+        arrowprops=dict(arrowstyle="->", color="red", alpha=0.7),
+        fontsize=10,
+        ha="center",
+    )
+    ax2.annotate(
+        "Significant Drop",
+        xy=("Q5", 32),
+        xytext=("Q4", 40),
+        arrowprops=dict(arrowstyle="->", color="red", alpha=0.7),
+        fontsize=10,
+        ha="center",
+    )
+
+    plt.tight_layout()
+
+    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_chart2:
+        fig2.savefig(temp_chart2.name, dpi=150, bbox_inches="tight")
+        plt.close(fig2)
+
+        chart_image2 = RLImage(temp_chart2.name, width=6 * inch, height=3.5 * inch)
+        story.append(chart_image2)
+        story.append(Spacer(1, 12))
+
+    # Section 3: Detailed Analysis
+    story.append(Paragraph("3. Trend Analysis and Insights", styles["Heading2"]))
+    trends_text = """
+    The comprehensive trend analysis reveals several key insights:
+    
+    1. Performance Trajectory: The actual performance closely followed forecasted patterns through Q3,
+    with Q4 showing exceptional results that exceeded both targets and forecasts.
+    
+    2. Q5 Anomaly: The dramatic decline in Q5 represents a significant deviation from all projections,
+    suggesting external factors or methodological changes that require investigation.
+    
+    3. Forecasting Accuracy: The forecast model demonstrated high accuracy for Q1-Q4, with prediction
+    errors remaining within acceptable ranges until the Q5 anomaly.
+    """
+    story.append(Paragraph(trends_text, styles["Normal"]))
+    story.append(Spacer(1, 12))
+
+    # Chart 3: Heatmap-style visualization using matplotlib
+    fig3, ax3 = plt.subplots(figsize=(8, 5))
+
+    # Create sample correlation matrix data
+    metrics = ["Accuracy", "Speed", "Quality", "Efficiency", "Satisfaction"]
+    correlation_data = np.array(
+        [
+            [1.0, 0.8, 0.9, 0.7, 0.6],
+            [0.8, 1.0, 0.6, 0.9, 0.5],
+            [0.9, 0.6, 1.0, 0.5, 0.8],
+            [0.7, 0.9, 0.5, 1.0, 0.4],
+            [0.6, 0.5, 0.8, 0.4, 1.0],
+        ]
+    )
+
+    im = ax3.imshow(correlation_data, cmap="RdYlBu_r", aspect="auto", vmin=-1, vmax=1)
+
+    # Add colorbar
+    cbar = plt.colorbar(im, ax=ax3)
+    cbar.set_label("Correlation Coefficient", rotation=270, labelpad=20)
+
+    # Set ticks and labels
+    ax3.set_xticks(range(len(metrics)))
+    ax3.set_yticks(range(len(metrics)))
+    ax3.set_xticklabels(metrics, rotation=45, ha="right")
+    ax3.set_yticklabels(metrics)
+
+    # Add correlation values as text
+    for i in range(len(metrics)):
+        for j in range(len(metrics)):
+            text = ax3.text(
+                j, i, f"{correlation_data[i, j]:.1f}", ha="center", va="center", color="black", fontweight="bold"
+            )
+
+    ax3.set_title("Performance Metrics Correlation Matrix", fontsize=14, fontweight="bold", pad=20)
+    plt.tight_layout()
+
+    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_chart3:
+        fig3.savefig(temp_chart3.name, dpi=150, bbox_inches="tight")
+        plt.close(fig3)
+
+        chart_image3 = RLImage(temp_chart3.name, width=6.5 * inch, height=4 * inch)
+        story.append(chart_image3)
+        story.append(Spacer(1, 12))
+
+    # Final section
+    story.append(Paragraph("4. Recommendations and Future Directions", styles["Heading2"]))
+    recommendations_text = """
+    Based on the comprehensive analysis of performance data and correlation patterns:
+    
+    • Immediate Actions: Investigate root causes of Q5 performance decline and implement
+      corrective measures to restore performance to Q4 levels.
+    
+    • Process Optimization: Leverage the strong correlation between Speed and Efficiency (0.9)
+      to develop integrated improvement strategies.
+    
+    • Quality Assurance: The high correlation between Accuracy and Quality (0.9) suggests
+      that accuracy improvements will directly impact overall quality metrics.
+    
+    • Forecasting Enhancement: Update prediction models to account for identified anomalies
+      and improve resilience to external factors.
+    
+    • Monitoring Strategy: Implement real-time dashboards to track performance metrics
+      and enable proactive intervention before significant declines occur.
+    
+    This mixed content document demonstrates the integration of textual analysis with
+    visual data representations, creating comprehensive test scenarios for PDF extraction
+    systems that must handle both content types effectively while maintaining their
+    contextual relationships.
+    """
+    story.append(Paragraph(recommendations_text, styles["Normal"]))
+
+    doc.build(story)
+
+    # Clean up temporary chart files
+    Path(temp_chart1.name).unlink()
+    Path(temp_chart2.name).unlink()
+    Path(temp_chart3.name).unlink()
+
+    print(f"Generated enhanced mixed content PDF: {filename}")
+    return filename
+
+
+def generate_multi_column_pdf():
+    """Generate a PDF with multi-column layout for advanced testing."""
+    test_data_dir = create_test_data_dir()
+    filename = test_data_dir / "multi_column_document.pdf"
+
+    # Create custom document template with two-column layout
+    doc = BaseDocTemplate(str(filename), pagesize=letter)
+
+    # Define frame layout for two columns
+    frame_width = (letter[0] - 1.5 * inch) / 2  # Split width between two columns
+    frame_height = letter[1] - 1.5 * inch
+
+    left_frame = Frame(
+        0.75 * inch,
+        0.75 * inch,
+        frame_width,
+        frame_height,
+        leftPadding=6,
+        rightPadding=6,
+        topPadding=6,
+        bottomPadding=6,
+    )
+    right_frame = Frame(
+        0.75 * inch + frame_width + 0.25 * inch,
+        0.75 * inch,
+        frame_width,
+        frame_height,
+        leftPadding=6,
+        rightPadding=6,
+        topPadding=6,
+        bottomPadding=6,
+    )
+
+    # Create page template
+    two_column_template = PageTemplate(id="TwoColumn", frames=[left_frame, right_frame])
+    doc.addPageTemplates([two_column_template])
+
+    styles = getSampleStyleSheet()
+    story = []
+
+    # Title (spans both columns)
+    title_style = ParagraphStyle(
+        "CustomTitle", parent=styles["Heading1"], fontSize=20, spaceAfter=20, alignment=1, textColor=colors.darkblue
+    )
+    story.append(Paragraph("Multi-Column Document Layout Test", title_style))
+    story.append(Spacer(1, 15))
+
+    # Add horizontal rule
+    story.append(HRFlowable(width="100%", thickness=2, color=colors.darkblue))
+    story.append(Spacer(1, 10))
+
+    # Column 1 content
+    story.append(Paragraph("Column 1: Research Overview", styles["Heading2"]))
+    col1_text1 = """
+    This document demonstrates multi-column layout capabilities for PDF extraction testing.
+    The text flows naturally between columns, creating a newspaper-like or academic journal
+    format that challenges extraction algorithms to properly maintain reading order and
+    column separation.
+    """
+    story.append(Paragraph(col1_text1, styles["Normal"]))
+    story.append(Spacer(1, 10))
+
+    story.append(Paragraph("Methodology", styles["Heading3"]))
+    col1_text2 = """
+    Our research methodology incorporates both quantitative and qualitative approaches.
+    The quantitative component involves statistical analysis of large datasets, while
+    the qualitative aspect includes expert interviews and case study analysis.
+    """
+    story.append(Paragraph(col1_text2, styles["Normal"]))
+    story.append(Spacer(1, 10))
+
+    # Table in first column
+    col1_table_data = [
+        ["Phase", "Duration"],
+        ["Planning", "2 weeks"],
+        ["Execution", "6 weeks"],
+        ["Analysis", "3 weeks"],
+        ["Reporting", "1 week"],
+    ]
+
+    col1_table = Table(col1_table_data, colWidths=[1.2 * inch, 1 * inch])
+    col1_table.setStyle(
+        TableStyle(
+            [
+                ("BACKGROUND", (0, 0), (-1, 0), colors.lightblue),
+                ("FONTNAME", (0, 0), (-1, 0), "Helvetica-Bold"),
+                ("FONTSIZE", (0, 0), (-1, -1), 9),
+                ("ALIGN", (0, 0), (-1, -1), "CENTER"),
+                ("GRID", (0, 0), (-1, -1), 1, colors.black),
+                ("VALIGN", (0, 0), (-1, -1), "MIDDLE"),
+            ]
+        )
+    )
+
+    story.append(col1_table)
+    story.append(Spacer(1, 15))
+
+    story.append(Paragraph("Data Collection", styles["Heading3"]))
+    col1_text3 = """
+    Data collection procedures follow established protocols to ensure reliability and
+    validity. Multiple sources are utilized including surveys, interviews, and
+    observational studies. Quality control measures are implemented at each stage
+    to maintain data integrity.
+    """
+    story.append(Paragraph(col1_text3, styles["Normal"]))
+
+    # Force column break
+    story.append(Spacer(1, 20))
+    story.append(Paragraph("Statistical Analysis", styles["Heading3"]))
+    col1_text4 = """
+    Statistical analysis employs both descriptive and inferential techniques.
+    Descriptive statistics provide overview of data characteristics, while
+    inferential methods test hypotheses and examine relationships between variables.
+    """
+    story.append(Paragraph(col1_text4, styles["Normal"]))
+
+    # Column 2 content (this will flow to the next column)
+    story.append(Paragraph("Column 2: Results and Discussion", styles["Heading2"]))
+    col2_text1 = """
+    The results section presents findings from our comprehensive analysis. Key insights
+    emerged from both quantitative metrics and qualitative observations. These findings
+    have significant implications for future research directions and practical applications
+    in the field.
+    """
+    story.append(Paragraph(col2_text1, styles["Normal"]))
+    story.append(Spacer(1, 10))
+
+    story.append(Paragraph("Key Findings", styles["Heading3"]))
+
+    # Bullet points
+    findings = [
+        "Significant improvement in accuracy metrics (p < 0.05)",
+        "Reduced processing time by 40% compared to baseline",
+        "Enhanced user satisfaction scores across all demographics",
+        "Scalability demonstrated up to 10,000 concurrent users",
+        "Cost reduction of 25% in operational expenses",
+    ]
+
+    for finding in findings:
+        story.append(Paragraph(f"• {finding}", styles["Normal"]))
+
+    story.append(Spacer(1, 10))
+
+    # Results table in second column
+    col2_table_data = [
+        ["Metric", "Before", "After", "Change"],
+        ["Accuracy", "78.5%", "92.3%", "+17.6%"],
+        ["Speed", "245ms", "134ms", "-45.3%"],
+        ["Memory", "128MB", "76MB", "-40.6%"],
+        ["Satisfaction", "3.2/5", "4.7/5", "+46.9%"],
+    ]
+
+    col2_table = Table(col2_table_data, colWidths=[0.8 * inch, 0.6 * inch, 0.6 * inch, 0.6 * inch])
+    col2_table.setStyle(
+        TableStyle(
+            [
+                ("BACKGROUND", (0, 0), (-1, 0), colors.darkgreen),
+                ("TEXTCOLOR", (0, 0), (-1, 0), colors.whitesmoke),
+                ("FONTNAME", (0, 0), (-1, 0), "Helvetica-Bold"),
+                ("FONTSIZE", (0, 0), (-1, -1), 8),
+                ("ALIGN", (0, 0), (-1, -1), "CENTER"),
+                ("BACKGROUND", (0, 1), (-1, -1), colors.lightgreen),
+                ("GRID", (0, 0), (-1, -1), 1, colors.black),
+                ("VALIGN", (0, 0), (-1, -1), "MIDDLE"),
+            ]
+        )
+    )
+
+    story.append(col2_table)
+    story.append(Spacer(1, 10))
+
+    story.append(Paragraph("Discussion", styles["Heading3"]))
+    col2_text2 = """
+    The discussion interprets results in context of existing literature and theoretical
+    frameworks. Notable patterns emerge when comparing our findings with previous studies.
+    The implications extend beyond immediate applications to broader methodological
+    considerations for future research.
+    """
+    story.append(Paragraph(col2_text2, styles["Normal"]))
+    story.append(Spacer(1, 10))
+
+    story.append(Paragraph("Limitations", styles["Heading3"]))
+    col2_text3 = """
+    Several limitations should be acknowledged. Sample size constraints may affect
+    generalizability. Temporal factors could influence long-term validity. Geographic
+    scope may limit applicability to other regions. Despite these limitations, the
+    study provides valuable insights and establishes foundation for future work.
+    """
+    story.append(Paragraph(col2_text3, styles["Normal"]))
+    story.append(Spacer(1, 10))
+
+    story.append(Paragraph("Future Work", styles["Heading3"]))
+    col2_text4 = """
+    Future research should address identified limitations and explore new directions.
+    Longitudinal studies could examine temporal stability. Cross-cultural validation
+    would enhance generalizability. Integration with emerging technologies presents
+    exciting opportunities for innovation.
+    """
+    story.append(Paragraph(col2_text4, styles["Normal"]))
+
+    doc.build(story)
+    print(f"Generated multi-column PDF: {filename}")
+    return filename
+
+
+def main():
+    """Generate all test PDF files."""
+    print("Generating test PDF files...")
+
+    # Create test data directory
+    test_data_dir = create_test_data_dir()
+    print(f"Test data directory: {test_data_dir}")
+
+    # Generate all test PDFs
+    text_pdf = generate_text_based_pdf()
+    scanned_pdf = generate_scanned_image_pdf()
+    mixed_pdf = generate_mixed_content_pdf()
+    multi_column_pdf = generate_multi_column_pdf()
+
+    print("\nGenerated test PDF files:")
+    print(f"1. Text-based: {text_pdf}")
+    print(f"2. Scanned: {scanned_pdf}")
+    print(f"3. Mixed content: {mixed_pdf}")
+    print(f"4. Multi-column: {multi_column_pdf}")
+    print("\nTest PDF generation complete!")
+
+
+if __name__ == "__main__":
+    main()
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