From 5a9faf5d9ff4197787e684d42e79885cd4c415de Mon Sep 17 00:00:00 2001 From: tintin Date: Wed, 12 Jul 2023 11:58:51 +0900 Subject: [PATCH 1/2] update readme --- .DS_Store | Bin 6148 -> 6148 bytes README.md | 23 ++++++++++++------ ...{TextlineRecognition.jpg => kindai_v1.jpg} | Bin images/kindai_v2.png | Bin 0 -> 189931 bytes 4 files changed, 15 insertions(+), 8 deletions(-) rename images/{TextlineRecognition.jpg => kindai_v1.jpg} (100%) create mode 100644 images/kindai_v2.png diff --git a/.DS_Store b/.DS_Store index f09902034b81a4d01a5544ecb45738d2e99ec556..2fb2eb824ac5ba5e3bde81c80936d1080ab789fd 100644 GIT binary patch delta 202 zcmZoMXfc=|#>B!ku~2NHo}wrl0|Nsi1A_oVQh9MfQcivnkiTQ|N9N_sk{~G-h7^WG zh7yKEWa-JG%!Qm1)zzkkmO2V%lP56CGImW~$1Km-J^3z^G*DkALoQHPIzuW$F){iC ryMZ>L=-T{}NuOymI|n}p(4|1vd}p4_FXG4nbSM*0DZ}Onku}T!^64>y delta 103 zcmZoMXfc=|#>B)qu~2NHo}wrV0|Nsi1A_oVQh9MfQcivnkiTZD14l;>lbA-qmW&kA5 B84>^h diff --git a/README.md b/README.md index 9da44e1..94def31 100644 --- a/README.md +++ b/README.md @@ -4,20 +4,28 @@ # Kindai-OCR OCR system for recognizing modern Japanese magazines +# Updates: +Kindai V2.0 employed Transformer OCR for text recognition. Transformer OCR was trained on NDL and CODH datasets. + ## About -This repo contains an OCR sytem for converting modern Japanese images to text. -This is a result of [N2I project](http://codh.rois.ac.jp/collaboration/#n2i) for digitization of modern Japanese documents. +This repo contains an OCR system for converting modern Japanese images to text. The software has been developed by Dr. Anh Duc Le, while he was working for ROIS-DS Center for Open Data in the Humanities. -The system has 2 main modules: text line extraction and text line recognition. The overall architechture is shown in the below figures. +The system has 2 main modules: text line extraction and text line recognition. The overall architecture is shown in the below figures. ![alt text](https://github.com/ducanh841988/Kindai-OCR/blob/master/images/TextlineExtraction.jpg "text line extraction") For text line extraction, we retrain the CRAFT (Character Region Awareness for Text Detection) on 1000 annotated images provided by Center for Research and Development of Higher Education, The University of Tokyo. -![alt text](https://github.com/ducanh841988/Kindai-OCR/blob/master/images/TextlineRecognition.jpg "text line recognition") +![alt text](https://github.com/ducanh841988/Kindai-OCR/blob/master/images/kindai_v1.jpg "text line recognition with attention model") +Text line recognition with attention model in Kindai V1.0 +![alt text](https://github.com/ducanh841988/Kindai-OCR/blob/master/images/kindai_v2.png "text line recognition with ") +Text line recognition with Transformer in Kindai V2.0 Text line recognition, For Kindai V1.0, we employ the attention-based encoder-decoder on our previous publication. We train the text line recognition on 1000 annotated images and 1600 unannotated images provided by Center for Research and Development of Higher Education, The University of Tokyo and National Institute for Japanese Language and Linguistics, respectively. -For Kindai V2.0, we trained a transformer with more data from National Diet Library and The Center for Open Data in The Humanities. +For Kindai V2.0, we trained a transformer with more data from National Diet Library (NDL) and The Center for Open Data in The Humanities (CODH). +[NDL dataset](https://github.com/ndl-lab/pdmocrdataset-part2) contains 3,997 pages, 103,256 lines and [CODH dataset](http://codh.rois.ac.jp/modern-magazine/dataset/) contains 1985 pages and 59,465 lines. + + ## Installing Kindai OCR @@ -36,8 +44,7 @@ editdistance==0.5.3 ## Running Kindai OCR - You should first download the pre_trained models and put them into ./pretrain/ folder. -[VGG model](https://drive.google.com/file/d/1_A1dEFKxyiz4Eu1HOCDbjt1OPoEh90qr/view?usp=sharing), [CRAFT model](https://drive.google.com/file/d/1-9xt_jjs4btMrz5wzrU1-kyp2c6etFab/view?usp=sharing), [OCR V1.0 model](https://drive.google.com/file/d/1mibg7D2D5rvPhhenLeXNilSLMBloiexl/view?usp=sharing) -[OCR V2.0 model] (https://drive.google.com/file/d/1cq4PwPS2mXXRjOApst2i7n4G3mBSVqpI/view?usp=drive_link) +[VGG model](https://drive.google.com/file/d/1_A1dEFKxyiz4Eu1HOCDbjt1OPoEh90qr/view?usp=sharing), [CRAFT model](https://drive.google.com/file/d/1-9xt_jjs4btMrz5wzrU1-kyp2c6etFab/view?usp=sharing), [OCR V1.0 model](https://drive.google.com/file/d/1mibg7D2D5rvPhhenLeXNilSLMBloiexl/view?usp=sharing), [OCR V2.0 model](https://drive.google.com/file/d/1cq4PwPS2mXXRjOApst2i7n4G3mBSVqpI/view?usp=drive_link) - Copy your images into ./data/test/ folder - run the following script to recognize images: `python test_kindai_1.0.py` @@ -50,7 +57,7 @@ editdistance==0.5.3 - An example result from our OCR system - ## Running Kindai OCR + ## Citation If you find Kindai OCR useful in your research, please consider citing: Anh Duc Le, Daichi Mochihashi, Katsuya Masuda, Hideki Mima, and Nam Tuan Ly. 2019. Recognition of Japanese historical text lines by an attention-based encoder-decoder and text line generation. In Proceedings of the 5th International Workshop on Historical Document Imaging and Processing (HIP ’19). Association for Computing Machinery, New York, NY, USA, 37–41. 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94def31..f2b8ffc 100644 --- a/README.md +++ b/README.md @@ -12,24 +12,28 @@ Kindai V2.0 employed Transformer OCR for text recognition. Transformer OCR was t This repo contains an OCR system for converting modern Japanese images to text. The software has been developed by Dr. Anh Duc Le, while he was working for ROIS-DS Center for Open Data in the Humanities. The system has 2 main modules: text line extraction and text line recognition. The overall architecture is shown in the below figures. -![alt text](https://github.com/ducanh841988/Kindai-OCR/blob/master/images/TextlineExtraction.jpg "text line extraction") -For text line extraction, we retrain the CRAFT (Character Region Awareness for Text Detection) on 1000 annotated images provided by Center for Research and Development of Higher Education, The University of Tokyo. -![alt text](https://github.com/ducanh841988/Kindai-OCR/blob/master/images/kindai_v1.jpg "text line recognition with attention model") -Text line recognition with attention model in Kindai V1.0 +### Text line extraction +We retrain the CRAFT (Character Region Awareness for Text Detection) on 1000 annotated images provided by Center for Research and Development of Higher Education, The University of Tokyo. + + + +### Text line recognition +For Kindai V1.0, we employ the attention-based encoder-decoder on our previous publication. We train the text line recognition on 1000 annotated images and 1600 unannotated images provided by Center for Research and Development of Higher Education, The University of Tokyo and National Institute for Japanese Language and Linguistics, respectively. + + -![alt text](https://github.com/ducanh841988/Kindai-OCR/blob/master/images/kindai_v2.png "text line recognition with ") -Text line recognition with Transformer in Kindai V2.0 -Text line recognition, -For Kindai V1.0, we employ the attention-based encoder-decoder on our previous publication. We train the text line recognition on 1000 annotated images and 1600 unannotated images provided by Center for Research and Development of Higher Education, The University of Tokyo and National Institute for Japanese Language and Linguistics, respectively. For Kindai V2.0, we trained a transformer with more data from National Diet Library (NDL) and The Center for Open Data in The Humanities (CODH). -[NDL dataset](https://github.com/ndl-lab/pdmocrdataset-part2) contains 3,997 pages, 103,256 lines and [CODH dataset](http://codh.rois.ac.jp/modern-magazine/dataset/) contains 1985 pages and 59,465 lines. +[NDL dataset](https://github.com/ndl-lab/pdmocrdataset-part2) contains 3,997 pages, 103,256 lines and [CODH dataset](http://codh.rois.ac.jp/modern-magazine/dataset/) contains 1985 pages and 59,465 lines. + + + ## Installing Kindai OCR -Python==3.7.11 +```Python==3.7.11 torch==1.7.0 torchvision==0.8.1 opencv-python==3.4.2.17 @@ -39,7 +43,8 @@ Polygon3 pillow==4.3.0 pytorch-lightning==1.3.5 einops==0.3.0 -editdistance==0.5.3 +editdistance==0.5.3 +``` ## Running Kindai OCR @@ -55,7 +60,7 @@ editdistance==0.5.3 - using --cuda = True for GPU device and Fasle for CPU device - using --canvas_size ot set image size for text line detection - An example result from our OCR system - + ## Citation If you find Kindai OCR useful in your research, please consider citing: