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zZQD5a(6qoi(aukKH7hQ;EYQuk8O2b*n z7qT5i;}`1ArpweT>u($-J~KUMZBy1mF2wlZETUg%yh-+#4wB-Xtmq46wL^W54r?z& z4<<{o{*BU{KX(PE+p$`13%?+^%=b^<$LPQE?JU)Hm;kC2-j&vGpuJ+7xstq|JiU%0I3sY z^T)OzpJI#ZO4)^tng0XMVLV%Nj;7^>DXBCyk>|0aO>jTw`PNKrQq=MMP_o@I$-2=$ zgY-5`Nqtk#XXX1Xjd)~qQL}T@Tj^>fmxFuPD1BFF!(ZH|49(szK#=T7XemkUc-uba zxcBfq%kJ3?yL^yp_sVrh1jmBJi;+gE<#t$!r(MHdr1TMmJROx?d%cXWQ`HUM)7SsO zfX%mgo~3?j7;M_zawU>5%Y@|o{<&zTNeEeE+jtBOmuleY4ScPl5!>wk zf5k|%faFFUf>RRjTx;ZJ%<_7s!v<(D1J5UC*XI-Yj*g-KcaXGM<=q3ach~B&zhoMK>GA5O8YFS=THy0hs~{YoRiPeE6W zc*Xr Demo URL: https://share.streamlit.io/ds4v/nomnasite/main/app.py +- Audio: [ĐÀ LỊCH SỬ- Tóm tắt lịch sử Việt Nam - Từ Nguyễn Phương](https://youtu.be/1dwFsbbPJ80?t=686) +- Demo: https://share.streamlit.io/ds4v/nomnasite/main/app.py ## I. Tổng quan ### 1. Giới thiệu -Tiếng Việt diệu kì với ngữ âm cực kỳ phong phú cùng hệ thống chữ viết giàu mạnh nhất vùng Đông Á đã trải qua hành trình từ *chữ Nho* hay *chữ Hán* cho đến *chữ Nôm* và cuối cùng là *chữ Quốc Ngữ*, và đi cùng với mỗi loại chữ ấy là những trang sử vẻ vang của dân tộc. +Tiếng Việt diệu kì với ngữ âm cực kỳ phong phú cùng hệ thống chữ viết giàu mạnh nhất vùng Đông Á đã trải qua hành trình từ *chữ Nho* hay *chữ Hán* cho đến *chữ Nôm* và cuối cùng là *chữ Quốc Ngữ*. Đi cùng với mỗi loại chữ ấy là những trang sử vẻ vang của dân tộc. Sau khi kết thúc Nghìn năm Bắc thuộc, ông cha ta với ý thức tự chủ ngôn ngữ, đã sáng tạo ra [chữ Nôm](https://vi.wikipedia.org/wiki/Ch%E1%BB%AF_N%C3%B4m) dựa trên cơ sở chữ Hán được đọc theo âm Hán-Việt. Và đây cũng là công cụ duy nhất hoàn toàn ghi lại lịch sử văn hoá của dân tộc trong khoảng 10 thế kỷ. Tuy nhiên, di sản này hiện tại có nguy cơ tiêu vong.

    "Ngày nay, trên thế giới chưa có đến 100 người đọc được chữ Nôm. Một phần to tát
    của lịch sử Việt Nam như thế nằm ngoài tầm tay của 80 triệu người nói tiếng Việt."

    -

    (Theo Hội Bảo tồn di sản chữ Nôm Việt Nam – VNPF)

    +

    (Hội Bảo tồn di sản chữ Nôm Việt Nam – VNPF)

    -Để sử dụng nguồn tri thức khổng lồ này, chúng cần phải được số hóa và dịch sang Quốc Ngữ hiện đại. Do việc dịch thuật khó khăn và tốn nhiều thời gian cùng số lượng chuyên gia hạn chế nên các nổ lực này không thể thực hiện trong thời gian ngắn. Để có thể tăng tốc quá trình số hóa này. Các kỹ thuật về OCR chính là chìa khóa giúp mọi công trình chính trong Hán-Nôm thành sẵn có trực tuyến. +Để sử dụng nguồn tri thức khổng lồ này, chúng cần phải được số hóa và dịch sang Quốc Ngữ hiện đại. Do việc dịch thuật khó khăn và tốn nhiều thời gian cùng số lượng chuyên gia hạn chế nên các nổ lực này không thể thực hiện trong thời gian ngắn. + +👉 Để có thể tăng tốc quá trình số hóa này, các kỹ thuật về OCR chính là chìa khóa giúp mọi công trình chính trong Hán-Nôm thành sẵn có trực tuyến. ### 2. Các công việc đã thực hiện - Xây dựng thành công bộ dữ liệu [NomNaOCR](https://www.kaggle.com/datasets/quandang/nomnaocr): - - Giải quyết 2 bài toán **Text Detection** và **Text Recognition** cho chữ Hán-Nôm. - - **Lớn nhất Việt Nam hiện tại** với tổng cộng **2953 Page** và **38,318 Patch**. + - Giải quyết 2 bài toán **Text Detection** và **Text Recognition** cho các tài liệu lịch sử viết bằng Hán-Nôm. + - Có kích thước **lớn nhất Việt Nam hiện tại** với tổng cộng **2953 Page** và **38318 Patch**. - Xây dựng thành công Pipeline cho các tác vụ OCR trên văn bản Hán-Nôm bằng phương pháp Deep Learning. - ![](./Assets/ocr_pipeline.jpg) + ![](./Assets/ocr_pipeline1.jpg) -- Cài đặt và thử nghiệm các mô hình trên **mức chuỗi thay vì từng kí tự** như các công trình trước. +- Cài đặt và thử nghiệm các mô hình trên **mức chuỗi** (Sequence level). Điều này chẳng những giúp tiết kiệm được chi phí gán nhãn mà còn giúp giữ lại được ngữ nghĩa trong câu thay vì chỉ thực hiện cho từng ký tự riêng lẻ như đa phần các công trình trước. Các bạn có thể xem qua các project open-source này nếu cần triển khai theo **mức kí tự** (Character level): + - https://github.com/trhgquan/OCR_chu_nom + - https://www.kaggle.com/competitions/kuzushiji-recognition 👉 Xem thêm [thesis_vi.pdf](./thesis_vi.pdf) và [slide_vi.pptx](./slide_vi.pptx) để biết thêm thông tin chi tiết. @@ -36,14 +41,15 @@ Sau khi kết thúc Nghìn năm Bắc thuộc, ông cha ta với ý thức tự ![](./Assets/data_pipeline_vi.jpg) +**Lưu ý**: Các bạn nên sử dụng font [NomNaTong](http://www.nomfoundation.org/nom-tools/Nom-Font) để có thể đọc các nội dung Hán-Nôm 1 cách tốt nhất. -> Dataset URL: https://www.kaggle.com/datasets/quandang/nomnaocr +> Dataset: https://www.kaggle.com/datasets/quandang/nomnaocr ### 1. Quy trình thu thập dữ liệu [VNPF](http://www.nomfoundation.org) đã số hóa cho rất nhiều tác phẩm Hán-Nôm nổi tiếng có giá trị lịch sử cao. Để có thể sử dụng được khối tài nguyên vô giá trên, mình sử dụng [Automa](https://www.automa.site) để tạo một [luồng thu thập tự động](./Data%20collection/workflow.json) gồm: - Các hình ảnh lẫn URL của chúng. -- Các nội dung được phiên dịch gồm các ký tự Hán-Nôm kỹ thuật số và phần dịch Quốc ngữ của chúng (nếu có). +- Các nội dung được phiên dịch gồm các ký tự Hán-Nôm kỹ thuật số và phần dịch Quốc Ngữ của chúng (nếu có). https://user-images.githubusercontent.com/50880271/195821433-59c35cec-af04-4706-9aa9-a71b000e39a4.mp4 @@ -63,7 +69,7 @@ https://user-images.githubusercontent.com/50880271/195821433-59c35cec-af04-4706- - `nom.txt`: chứa các text chữ Hán-Nôm. - `modern.txt`: chứa các phiên âm tương ứng với file `nom.txt`. -[*] Còn về phần download hình ảnh, mình chỉ đơn giản sử dụng tính năng `Tải xuống hàng loạt` của [Internet Download Manager](https://www.internetdownloadmanager.com/). +[*] Còn về phần download hình ảnh, mình chỉ đơn giản sử dụng tính năng Tải xuống hàng loạt của [Internet Download Manager](https://www.internetdownloadmanager.com/). Xem thêm video hướng dẫn [tại đây](https://youtu.be/UBItV0g25vQ). #### b. Các tác phẩm đã thu thập @@ -86,9 +92,9 @@ https://user-images.githubusercontent.com/50880271/195821433-59c35cec-af04-4706- Nhóm mình sử dụng [PPOCRLabel](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/PPOCRLabel/README.md) thuộc hệ sinh thái của [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) để gán tự động các `bounding box`. Tool này mặc định sử dụng [DBNet](https://github.com/MhLiao/DB) để phát hiện văn bản, đây cũng là mô hình nhóm mình sẽ thử nghiệm cho bài toán **Text Detection**. Ở đây, mình có phân tool này ra thành 2 bản: - [annotators.zip](./Data%20labeling/PPOCRLabel/annotators.zip): dành cho người gán nhãn, mình đã bỏ đi các chức năng không cần thiết như `Auto annotation`, ... để tránh việc nhấn tùm lum có thể gây sai sót khi gán nhãn cũng như để việc cài đặt dễ dàng và ít lỗi hơn. -- [composer.zip](./Data%20labeling/PPOCRLabel/composer.zip): dành cho người xây dựng guideline (Mình sẽ gọi là **Composer**) dùng để chạy `Auto annotation`, khá đầy đủ chức năng so với bản gốc. Mình có bỏ đi thao tác *recognition* khi chạy `Auto annotation` và sử dụng nhãn cho text là `TEMPORARY`. +- [composer.zip](./Data%20labeling/PPOCRLabel/composer.zip): dành cho người xây dựng guideline (Mình sẽ gọi là **Composer**) dùng để chạy `Auto annotation`, khá đầy đủ chức năng so với bản gốc. Mình có bỏ đi thao tác *Auto recognition* khi chạy `Auto annotation` và sử dụng nhãn cho text là `TEMPORARY`. -👉 Các Annotator sẽ thay thế nhãn `TEMPORARY` theo hướng dẫn trong các guideline dành cho [thơ](./Data%20labeling/Guideline-poem.pdf) và cho [văn xuôi](./Data%20labeling/Guideline-prose.pdf). Cuối cùng là thực hiện map nhãn thật thu thập từ [VNPF](http://www.nomfoundation.org). +👉 Các Annotator sẽ thay thế nhãn `TEMPORARY` theo hướng dẫn trong các guideline dành cho [thơ](./Data%20labeling/Guideline-poem.pdf) và cho [văn xuôi](./Data%20labeling/Guideline-prose.pdf). Cuối cùng là thực hiện map nhãn thật được thu thập từ [VNPF](http://www.nomfoundation.org). ![](./Assets/labeling.jpg) @@ -98,7 +104,7 @@ Tuy nhiên, với các ảnh trong [NomNaOCR](https://www.kaggle.com/datasets/qu - Sau đó đưa ảnh vào [PPOCRLabel](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/PPOCRLabel/README.md) để dự đoán các `bounding box`. - Khi dự đoán xong, chạy file [unrotated_convertor.py](./Data%20labeling/Auto%20annotation/unrotated_convertor.py) để xoay dọc các `bounding box` lại. -Sau khâu triển khai thực tế, bộ dữ liệu [NomNaOCR](https://www.kaggle.com/datasets/quandang/nomnaocr) được xử lý và thu được **2953 Page** (đã bỏ đi 1 Page scan lỗi và 2 Page trống). Bằng cách gán nhãn bán thủ công, nhóm mình đã thu được thêm **38,318 Patch**. Tiếp theo, nhóm mình sử dụng công thức từ bộ dữ liệu [IHR-NomDB](https://morphoboid.labri.fr/ihr-nom.html) để chia dữ liệu 1 cách hiệu quả nhất. Phần **Synthetic Nom String** thuộc bộ dữ liệu này cũng được dùng để thực hiện **Pretraining** cho các mô hình **Recognition**. +Sau khâu triển khai thực tế, bộ dữ liệu [NomNaOCR](https://www.kaggle.com/datasets/quandang/nomnaocr) được xử lý và thu được **2953 Page** (đã bỏ đi 1 Page scan lỗi và 2 Page trống). Bằng cách gán nhãn bán thủ công, nhóm mình đã thu được thêm **38318 Patch**. Tiếp theo, nhóm mình sử dụng công thức từ bộ dữ liệu [IHR-NomDB](https://morphoboid.labri.fr/ihr-nom.html) để chia dữ liệu 1 cách hiệu quả nhất. Phần **Synthetic Nom String** thuộc bộ dữ liệu này cũng được dùng để thực hiện **Pretraining** cho các mô hình **Recognition**. | **Tập dữ liệu** | **Số điểm dữ liệu** | **Tỉ lệ ký tự giao nhau** | |:-------------------:|:-------------------:|:-------------------------:| @@ -114,7 +120,6 @@ Sau khâu triển khai thực tế, bộ dữ liệu [NomNaOCR](https://www.kagg - Với Detection mình sử dụng [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) để huấn luyện với các file config tương ứng trong folder [Text detection](./Text%20detection/). - Với Recognition, trong quá trình **PreTraining** trên bộ **Synthetic Nom String** của [IHR-NomDB](https://morphoboid.labri.fr/ihr-nom.html), mình nhận thấy khi thực hiện **Skip Connection** (SC) cho **feature map** với 1 lớp **X** có cùng shape và nằm xa nó nhất sẽ cải thiện đáng kể hiệu suất mô hình. Vì vậy, mình có thử nghiệm 2 phương pháp **Skip Connection** cơ bản là **Addition** và **Concatenation** cho các mô hình khả thi nhất (tồn tại lớp **X** nói trên). -- Download font **NomNaTong** mới nhất [tại đây](http://www.nomfoundation.org/nom-tools/Nom-Font). 👉 Xem thêm [thesis_vi.pdf](./thesis_vi.pdf) và [slide_vi.pptx](./slide_vi.pptx) để biết thêm thông tin chi tiết. @@ -122,12 +127,14 @@ Sau khâu triển khai thực tế, bộ dữ liệu [NomNaOCR](https://www.kagg ![](./Assets/evaluation_pipeline_vi.jpg) -- Metrics đánh giá Text Detection và End-to-End: sử dụng 1 phương pháp mới tên là [CLEval](https://github.com/clovaai/CLEval) giúp đánh giá hiệu quả cho cả 2 giai đoạn phát hiện và nhận dạng văn bản (**End-to-End**). Ngoài ra, phương pháp này cũng có thể đánh giá được cho riêng **Text Detection** nên tùy vào bài toán mà [CLEval](https://github.com/clovaai/CLEval) sẽ có sự khác nhau trong các thành phần tính toán của nó. -- Metrics đánh giá với riêng Text Recognition: sử dụng các phương pháp tương tự với các công trình trước đó bao gồm: **Sequence Accuracy**, **Character Accuracy** và **Character Error Rate** (CER). -- Ngoài ra, với Recognition mình chỉ giữ lại output của những `notebook` của mô hình có kết quả tốt nhất trên tập **Validate** của [NomNaOCR](https://www.kaggle.com/datasets/quandang/nomnaocr) gồm: +- Metrics đánh giá Text Detection và End-to-End: sử dụng 1 phương pháp mới tên là [CLEval](https://github.com/clovaai/CLEval) giúp đánh giá hiệu quả cho cả 2 giai đoạn phát hiện và nhận dạng văn bản (End-to-End). Ngoài ra, phương pháp này cũng có thể đánh giá được cho riêng Text Detection nên tùy vào bài toán mà [CLEval](https://github.com/clovaai/CLEval) sẽ có sự khác nhau trong các thành phần tính toán của nó. +- Metrics đánh giá cho riêng Text Recognition: sử dụng các phương pháp tương tự với các công trình trước đó bao gồm: **Sequence Accuracy**, **Character Accuracy** và **Character Error Rate** (CER). +- Ngoài ra, với Recognition mình chỉ giữ lại output của những `notebook` hay mô hình có kết quả tốt nhất trên tập **Validate** của [NomNaOCR](https://www.kaggle.com/datasets/quandang/nomnaocr) gồm: - [CRNNxCTC.ipynb](./Text%20recognition/CRNNxCTC/CRNNxCTC.ipynb): có **Sequence Accuracy** cao nhất. - [SC-CNNxTransformer_finetune.ipynb](./Text%20recognition/TransformerOCR/SC-CNNxTransformer_finetune.ipynb): có **Character Accuracy** và **CER** cao nhất. + ![](./Assets/example_rec.jpg) + ## IV. Kết quả thực nghiệm
    @@ -156,7 +163,7 @@ Sau khâu triển khai thực tế, bộ dữ liệu [NomNaOCR](https://www.kagg ## V. Trân trọng cảm ơn -- Các bạn trong team gán nhãn, những người đã hào phóng hy sinh một phần thời gian biểu để tham gia vào nghiên cứu của nhóm cũng như giúp nhóm có thể hoàn thành bộ dữ liệu thật tốt, từ đó góp phần biến đề tài này thành hiện thực: +- Các bạn trong team gán nhãn, những người đã hào phóng hy sinh một phần thời gian biểu để tham gia vào nghiên cứu cũng như giúp nhóm có thể hoàn thành bộ dữ liệu thật tốt, từ đó góp phần biến đề tài này thành hiện thực: | Thành viên (KHDL - VNUHCM UIT) | Email | |-----------------------------------------------------------------------------------|:----------------------:| @@ -168,21 +175,33 @@ Sau khâu triển khai thực tế, bộ dữ liệu [NomNaOCR](https://www.kagg | [Nguyễn Minh Trí](https://www.facebook.com/profile.php?id=100010288122956) | 20522052@gm.uit.edu.vn | | [Phạm Phú Phước](https://www.facebook.com/profile.php?id=100010066906493) | 18521031@gm.uit.edu.vn | -- Bạn [Nguyễn Ngọc Thịnh](https://www.facebook.com/TanyaNguyen99) (Đông Phương học - VNUHCM USSH) đã giúp chúng tôi giải đáp những thắc mắc liên quan đến chữ Hán-Nôm trong đề tài này. +- Bạn [Nguyễn Ngọc Thịnh](https://www.facebook.com/TanyaNguyen99) (Đông Phương học - VNUHCM USSH) đã giúp mình giải đáp những thắc mắc liên quan đến chữ Hán-Nôm trong đề tài này. + +- Thầy [Nguyễn Đạt Phi](https://www.facebook.com/datphi.nguyen), người sáng lập kênh [HÙNG CA SỬ VIỆT](https://www.youtube.com/c/%C4%90%E1%BA%A0TPHIMEDIAOFFICIAL) đã truyền cho mình ngọn lửa tình yêu với lịch sử dân tộc, cũng như là nguồn cảm hứng đưa mình đến với đề tài này. Những câu chuyện về cha ông được kể lại qua giọng đọc truyền cảm của thầy đã trở thành món ăn tinh thần không thể thiếu. -- Thầy [Nguyễn Đạt Phi](https://www.facebook.com/datphi.nguyen), người sáng lập kênh [HÙNG CA SỬ VIỆT](https://www.youtube.com/c/%C4%90%E1%BA%A0TPHIMEDIAOFFICIAL) đã truyền cho mình ngọn lửa tình yêu với lịch sử dân tộc. Những câu chuyện về cha ông được kể lại qua giọng đọc truyền cảm của thầy đã trở thành món ăn tinh thần không thể thiếu. +- Cuối cùng, xin chân thành cảm ơn [VNPF](http://www.nomfoundation.org) vì những công trình tuyệt vời, góp phần bảo tồn di sản lịch sử và văn hóa dân tộc. ## VI. TODO -- [ ] Gia tăng về số lượng cho [NomNaOCR](https://www.kaggle.com/datasets/quandang/nomnaocr) bằng nhiều tác phẩm lịch sử Việt Nam khác. -- [ ] Sử dụng **Beam search** hay xa hơn nữa là tích hợp **Language model** để giải mã đầu ra cho **Text Recognition**, tham khảo từ các project của [Harald Scheidl](https://github.com/githubharald). - [ ] [NomNaOCRpp](https://github.com/ds4v/NomNaOCRpp): Thử nghiệm các mô hình hiện đại hơn hay các mô hình SOTA trên các bộ dữ liệu benchmark nổi tiếng như ICDAR [2013](https://paperswithcode.com/dataset/icdar-2013) và [2015](https://paperswithcode.com/dataset/icdar-2015). -- [ ] [NomNaNMT](https://github.com/ds4v/NomNaNMT): Phát triển bài toán dịch máy để chuyển đổi các nội dung Hán-Nôm sang Quốc Ngữ. +- [ ] [NomNaNMT](https://github.com/ds4v/NomNaNMT): Phát triển bài toán dịch máy để dịch các phiên âm Hán-Nôm và từ đó dịch sang Quốc Ngữ hiện đại. - [ ] [NomNaSite](https://github.com/ds4v/NomNaSite): Xây dựng WebApp để ứng dụng các bài toán đã triển khai vào thực tế. +- [ ] Sử dụng **Beam search** hay xa hơn nữa là **Language model** để giải mã đầu ra (decode) cho **Text Recognition**, tham khảo từ các project của [Harald Scheidl](https://github.com/githubharald). +- [ ] Ghi nhận lại các lỗi trên [VNPF](http://www.nomfoundation.org) vào 1 file. Trong quá trình làm dataset, nhóm mình có phát hiện 1 vài lỗi trong phần dịch của [VNPF](http://www.nomfoundation.org) như 1 số phần dịch không khớp với Page hiện tại, chữ dịch sai so với hình, dịch thừa chữ hoặc thiếu chữ, .... Sau đây là 1 vài ví dụ: + + | Miêu tả lỗi | Tác phẩm | Trang | Vị trí trong ảnh | Ghi chú | + |:-------------------------------------------:|:--------------------:|:-----:|:----------------:|:--------------------------------------------------------------:| + | Chữ 揆 trong từ điển không có nghĩa "cõi" | Truyện Kiều bản 1866 | 1 | Câu 1 | | + | Chữ 別 khác trong hình | Truyện Kiều bản 1866 | 9 | Câu 22 | Dị thể với chữ 别, đa phần các version từ 1902 về trước đều bị | + | Chữ 𥪞 khác trong hình | Truyện Kiều bản 1866 | 55 | Câu 15 | | + | Chữ 󰁳 khác trong hình | Truyện Kiều bản 1866 | 55 | Câu 15 | | + | Có 21 câu > 20 trong ảnh | Lục Vân Tiên | 6 | - | | + | Có 19 câu < 20 trong ảnh | Lục Vân Tiên | 7 | - | | + | Chữ thứ 5 bị hiển thị [?] | Lục Vân Tiên | 7 | Câu 10 | | ## VII. Tham khảo -- Sách [Đắm mình vào học sâu](https://d2l.aivivn.com/) dịch bởi nhóm Dịch thuật Việt Nam. +- Sách [Đắm mình vào học sâu](https://d2l.aivivn.com/) được dịch bởi nhóm Dịch thuật Việt Nam. - Các bài viết về OCR của anh [Phạm Bá Cường Quốc](https://pbcquoc.github.io). - Series [Optical Character Recognition](https://theailearner.com/optical-character-recognition) của [TheAILearner](https://theailearner.com). - Các bài viết về OCR của [Nanonets](https://nanonets.com):

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