Add --ignore-embedded-text (#47)

This PR adds the `--ignore-embedded-text` flag to the `scan` command so
that it is possible to ignore embedded text in document types that
support it (e.g. PDFs). The motivation for this feature is that some
PDFs have OCR results embedded that are low quality and should be
ignored.

The addition to `load_document` is tested via:

```
pytest -sv tests/test_end_to_end.py::test_run_with_ignore_embedded_text
```

Notably, the answer with Tesseract only is actually incorrect, because
it _seems_ Tesseract is missing entire columns.

src/docquery/cmd/scan.py

@@ -27,6 +27,12 @@ def build_parser(subparsers, parent_parser):
     parser.add_argument(
         "--ocr", choices=list(OCR_MAPPING.keys()), default=None, help="The OCR engine you would like to use"
     )
+    parser.add_argument(
+        "--ignore-embedded-text",
+        dest="use_embedded_text",
+        action="store_false",
+        help="Do not try and extract embedded text from document types that might provide it (e.g. PDFs)",
+    )
     parser.add_argument(
         "--classify",
         default=False,
@@ -58,7 +64,7 @@ def main(args):
     for p in paths:
         try:
             log.info(f"Loading {p}")
-            docs.append((p, load_document(str(p), ocr_reader=args.ocr)))
+            docs.append((p, load_document(str(p), ocr_reader=args.ocr, use_embedded_text=args.use_embedded_text)))
         except UnsupportedDocument as e:
             log.warning(f"Cannot load {p}: {e}. Skipping...")
 

src/docquery/document.py

@@ -101,9 +101,10 @@ def _generate_document_output(
 
 
 class PDFDocument(Document):
-    def __init__(self, b, ocr_reader, **kwargs):
+    def __init__(self, b, ocr_reader, use_embedded_text, **kwargs):
         self.b = b
         self.ocr_reader = ocr_reader
+        self.use_embedded_text = use_embedded_text
 
         super().__init__(**kwargs)
 
@@ -125,7 +126,7 @@ def context(self) -> Dict[str, List[Tuple["Image.Image", List[Any]]]]:
         boxes_by_page = []
         dimensions_by_page = []
         for i, page in enumerate(pdf.pages):
-            extracted_words = page.extract_words()
+            extracted_words = page.extract_words() if self.use_embedded_text else []
 
             if len(extracted_words) == 0:
                 words, boxes = self.ocr_reader.apply_ocr(images[i])
@@ -234,7 +235,7 @@ def context(self) -> Dict[str, List[Tuple["Image.Image", List[Any]]]]:
 
 
 @validate_arguments
-def load_document(fpath: str, ocr_reader: Optional[Union[str, OCRReader]] = None):
+def load_document(fpath: str, ocr_reader: Optional[Union[str, OCRReader]] = None, use_embedded_text=True):
     base_path = os.path.basename(fpath).split("?")[0].strip()
     doc_type = mimetypes.guess_type(base_path)[0]
     if fpath.startswith("http://") or fpath.startswith("https://"):
@@ -255,7 +256,7 @@ def load_document(fpath: str, ocr_reader: Optional[Union[str, OCRReader]] = None
         raise NoOCRReaderFound(f"{ocr_reader} is not a supported OCRReader class")
 
     if doc_type == "application/pdf":
-        return PDFDocument(b.read(), ocr_reader=ocr_reader)
+        return PDFDocument(b.read(), ocr_reader=ocr_reader, use_embedded_text=use_embedded_text)
     elif doc_type == "text/html":
         return WebDocument(fpath)
     else:

tests/test_end_to_end.py

@@ -67,6 +67,11 @@ class Example(BaseModel):
                 "question": "What are net sales for 2020?",
                 "answers": {
                     "LayoutLMv1": [{"score": 0.9429, "answer": "$ 3,750\n", "word_ids": [15, 16], "page": 0}],
+                    # (The answer with `use_embedded_text=False` relies entirely on Tesseract, and it is incorrect because it
+                    # misses 3,750 altogether.)
+                    "LayoutLMv1__use_embedded_text=False": [
+                        {"score": 0.3078, "answer": "$ 3,980", "word_ids": [11, 12], "page": 0}
+                    ],
                     "LayoutLMv1-Invoices": [{"score": 0.9956, "answer": "$ 3,750\n", "word_ids": [15, 16], "page": 0}],
                     "Donut": [{"answer": "$ 3,750"}],
                 },
@@ -132,3 +137,12 @@ def test_run_with_choosen_OCR_instance():
     for qa in example.qa_pairs:
         resp = pipe(question=qa.question, **document.context, top_k=1)
         assert nested_simplify(resp, decimals=4) == qa.answers["LayoutLMv1"]
+
+
+def test_run_with_ignore_embedded_text():
+    example = EXAMPLES[2]
+    document = load_document(example.path, use_embedded_text=False)
+    pipe = pipeline("document-question-answering", model=CHECKPOINTS["LayoutLMv1"])
+    for qa in example.qa_pairs:
+        resp = pipe(question=qa.question, **document.context, top_k=1)
+        assert nested_simplify(resp, decimals=4) == qa.answers["LayoutLMv1__use_embedded_text=False"]