Duplicate from liminghao1630/DocBank

Co-authored-by: Minghao Li <liminghao1630@users.noreply.huggingface.co>

.gitattributes

@@ -0,0 +1,68 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+DocBank_500K_ori_img.zip.001 filter=lfs diff=lfs merge=lfs -text
+DocBank_500K_ori_img.zip.002 filter=lfs diff=lfs merge=lfs -text
+DocBank_500K_ori_img.zip.003 filter=lfs diff=lfs merge=lfs -text
+DocBank_500K_ori_img.zip.004 filter=lfs diff=lfs merge=lfs -text
+DocBank_500K_ori_img.zip.005 filter=lfs diff=lfs merge=lfs -text
+DocBank_500K_ori_img.zip.006 filter=lfs diff=lfs merge=lfs -text
+DocBank_500K_ori_img.zip.007 filter=lfs diff=lfs merge=lfs -text
+DocBank_500K_ori_img.zip.008 filter=lfs diff=lfs merge=lfs -text
+DocBank_500K_ori_img.zip.009 filter=lfs diff=lfs merge=lfs -text
+DocBank_500K_ori_img.zip.010 filter=lfs diff=lfs merge=lfs -text

DocBank_500K_ori_img.zip.001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0884689be13ef8d07737197465b2c64b8d51d03adc83e880d7f056dcc937c5a0
+size 5368709120

DocBank_500K_ori_img.zip.002

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:49d662c0facb9c532477a0bb43a766e0eb7e0d8fc85296e74239cde68b12a7ab
+size 5368709120

DocBank_500K_ori_img.zip.003

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d551bd8952e7a45ee0d1a686bf8973a1bf2d5ff84df34d47005eab237e9826fd
+size 5368709120

DocBank_500K_ori_img.zip.004

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a2cdf0c42176ddd9ffac8daad9d3ba6046d8289405a0c7ce27ed0edb9daef738
+size 5368709120

DocBank_500K_ori_img.zip.005

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d81f4ec4bf9ce48847c1bbd24e65895a6318b066c0dcb0c507a6d5ce38b79f28
+size 5368709120

DocBank_500K_ori_img.zip.006

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:74049926b050c969bd3cc6773537e49303b2b4afcc8a02337e2ffae9d92ed343
+size 5368709120

DocBank_500K_ori_img.zip.007

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:029bda8274762824ea69d3970ff1672751acd172f1068345fb4e09db4a99237b
+size 5368709120

DocBank_500K_ori_img.zip.008

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6e7af66e8a2c85eaa5b86900f7d524f2c53b4714f54a4aa7273db407e4ab38df
+size 5368709120

DocBank_500K_ori_img.zip.009

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:47d6422678e0bffe1ee32e3a9b811721d893ac3e943973e8423ce03b4efcd3d1
+size 5368709120

DocBank_500K_ori_img.zip.010

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87d4695574d3414a4fa5548a462dffd089b9e42764de5a5eb567b408b75c5669
+size 2589288107

DocBank_500K_txt.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:984668c0241d68476ff1e793c5e9c5dc525c557538250130b8af969200491318
+size 3167771976

MSCOCO_Format_Annotation.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8666d9a8ae77e471309c27ea514b30cb4e21ce2418cb28c2640736aa75ac7816
+size 208969736

README.md

@@ -0,0 +1,116 @@
+---
+license: apache-2.0
+---
+# DocBank
+
+DocBank is a new large-scale dataset that is constructed using a weak supervision approach. It enables models to integrate both the textual and layout information for downstream tasks. The current DocBank dataset totally includes 500K document pages, where 400K for training, 50K for validation and 50K for testing. 
+
+[[GitHub](https://github.com/doc-analysis/DocBank)] [[Paper](https://arxiv.org/abs/2006.01038)]
+
+## News
+- **We update the license to Apache-2.0.**
+- **Our paper has been accepted in [COLING2020](https://coling2020.org/pages/accepted_papers_main_conference) and [the Camera-ready version paper](https://arxiv.org/abs/2006.01038) has been updated on arXiv.com**
+- **We provide a dataset loader named [DocBankLoader](https://github.com/doc-analysis/DocBankLoader) and it can also convert DocBank to the Object Detection models' format**
+- **DocBank is a natural extension of the TableBank ([repo](https://github.com/doc-analysis/TableBank), [paper](https://arxiv.org/abs/1903.01949)) dataset**
+- **LayoutLM ([repo](https://github.com/microsoft/unilm/tree/master/layoutlm), [paper](https://arxiv.org/abs/1912.13318)) is an effective pre-training method of text and layout and archives the SOTA result on DocBank**
+
+## Introduction
+
+For document layout analysis tasks, there have been some image-based document layout datasets, while most of them are built for computer vision approaches and they are difficult to apply to NLP methods. In addition, image-based datasets mainly include the page images and the bounding boxes of large semantic structures, which are not fine-grained token-level annotations. Moreover, it is also time-consuming and labor-intensive to produce human-labeled and fine-grained token-level text block arrangement. Therefore, it is vital to leverage weak supervision to obtain fine-grained labeled documents with minimum efforts, meanwhile making the data be easily applied to any NLP and computer vision approaches. 
+
+To this end, we build the DocBank dataset, a  document-level  benchmark with fine-grained token-level annotations for layout  analysis. Distinct from the conventional human-labeled datasets, our approach obtains high quality annotations in a simple yet effective way with weak supervision.
+
+## Statistics of DocBank
+The DocBank dataset consists of 500K document pages with 12 types of semantic units.
+
+### Semantic Structure Statistics of DocBank
+| Split | Abstract | Author | Caption |  Date | Equation | Figure | Footer |  List  | Paragraph | Reference | Section | Table | Title |  Total  |
+|:-----:|:--------:|:------:|:-------:|:-----:|:--------:|:------:|:------:|:------:|:---------:|:---------:|:-------:|:-----:|:-----:|:-------:|
+| Train |   25,387  |  25,909 |  106,723 |  6,391 |  161,140  |  90,429 |  38,482 |  44,927 |   398,086  |   44,813   |  180,774 | 19,638 | 21,688 |  400,000 |
+|       |   6.35%  |  6.48% |  26.68% | 1.60% |  40.29%  | 22.61% |  9.62% | 11.23% |   99.52%  |   11.20%  |  45.19% | 4.91% | 5.42% | 100.00% |
+|  Dev  |   3,164   |  3,286  |  13,443  |  797  |   20,154  |  11,463 |  4,804  |  5,609  |   49,759   |    5,549   |  22,666  |  2,374 |  2,708 |  50,000  |
+|       |   6.33%  |  6.57% |  26.89% | 1.59% |  40.31%  | 22.93% |  9.61% | 11.22% |   99.52%  |   11.10%  |  45.33% | 4.75% | 5.42% | 100.00% |
+|  Test |   3,176   |  3,277  |  13,476  |  832  |   20,244  |  11,378 |  4,876  |  5,553  |   49,762   |    5,641   |  22,384  |  2,505 |  2,729 |  50,000  |
+|       |   6.35%  |  6.55% |  26.95% | 1.66% |  40.49%  | 22.76% |  9.75% | 11.11% |   99.52%  |   11.28%  |  44.77% | 5.01% | 5.46% | 100.00% |
+| Total |   31,727  |  32,472 |  133,642 |  8,020 |  201,538  | 113,270 |  48,162 |  56,089 |   497,607  |   56,003   |  225,824 | 24,517 | 27,125 |  500,000 |
+|       |   6.35%  |  6.49% |  26.73% | 1.60% |  40.31%  | 22.65% |  9.63% | 11.22% |   99.52%  |   11.20%  |  45.16% | 4.90% | 5.43% | 100.00% |
+
+### Year Statistics of DocBank
+
+|  Year |  Train |         |  Dev  |         |  Test |         |   ALL  |         |
+|:-----:|:------:|:-------:|:-----:|:-------:|:-----:|:-------:|:------:|:-------:|
+|  2014 |  65,976 |  16.49% |  8,270 |  16.54% |  8,112 |  16.22% |  82,358 |  16.47% |
+|  2015 |  77,879 |  19.47% |  9,617 |  19.23% |  9,700 |  19.40% |  97,196 |  19.44% |
+|  2016 |  87,006 |  21.75% | 10,970 |  21.94% | 10,990 |  21.98% | 108,966 |  21.79% |
+|  2017 |  91,583 |  22.90% | 11,623 |  23.25% | 11,464 |  22.93% | 114,670 |  22.93% |
+|  2018 |  77,556 |  19.39% |  9,520 |  19.04% |  9,734 |  19.47% |  96,810 |  19.36% |
+| Total | 400,000 | 100.00% | 50,000 | 100.00% | 50,000 | 100.00% | 500,000 | 100.00% |
+
+### Comparison of DocBank with existing document layout analysis datasets
+|     Dataset     |  #Pages | #Units | Image-based? | Text-based? | Fine-grained? | Extendable? |
+|:---------------:|:-------:|:------:|:------------:|:-----------:|:-------------:|:-----------:|
+| Article Regions |   100   |    9   |       ✔      |      ✘      |       ✔       |      ✘      |
+|     GROTOAP2    | 119,334 |   22   |       ✔      |      ✘      |       ✘       |      ✘      |
+|    PubLayNet    | 364,232 |    5   |       ✔      |      ✘      |       ✔       |      ✘      |
+|    TableBank    | 417,234 |    1   |       ✔      |      ✘      |       ✔       |      ✔      |
+|     DocBank     | 500,000 |   12   |       ✔      |      ✔      |       ✔       |      ✔      |
+
+## Baseline
+As the dataset was fully annotated at token-level, we consider the document layout analysis task as a text-based sequence labeling task. 
+
+Under this setting, we evaluate three representative pre-trained language models on our dataset including BERT, RoBERTa and LayoutLM to validate the effectiveness of DocBank.
+
+To verify the performance of the models from different modalities on DocBank, we train the Faster R-CNN model on the object detection format of DocBank and unify its output with the sequence labeling models to evaluate.
+
+
+### Settings
+Our baselines of BERT and RoBERTa are built upon the HuggingFace's Transformers while the LayoutLM baselines are implemented with the codebase in [LayoutLM's official repository](https://aka.ms/layoutlm). We used 8 V100 GPUs with a batch size of 10 per GPU. It takes 5 hours to fine-tune 1 epoch on the 400K document pages. We used the BERT and RoBERTa tokenizers to tokenize the training samples and optimized the model with AdamW. The initial learning rate of the optimizer is 5e-5. We split the data into a max block size of N=512. We use the [Detectron2](https://github.com/facebookresearch/detectron2) to train the Faster R-CNN model on DocBank. We use the Faster R-CNN algorithm with the ResNeXt-101 as the backbone network architecture, where the parameters are pre-trained on the ImageNet dataset.
+
+### Results
+
+#### The evaluation results of BERT, RoBERTa and LayoutLM
+|         Models        | Abstract |  Author | Caption | Equation |  Figure |  Footer |   List  | Paragraph | Reference | Section |  Table  |  Title  | Macro average |
+|:---------------------:|:--------:|:-------:|:-------:|:--------:|:-------:|:-------:|:-------:|:---------:|:---------:|:-------:|:-------:|:-------:|:-------------:|
+|       bert-base       |  0.9294  | 0.8484  | 0.8629  |  0.8152  | 1.0000  | 0.7805  | 0.7133  |  0.9619   |  0.9310   | 0.9081  | 0.8296  | 0.9442  |    0.8770     |
+|      roberta-base     |  0.9288  | 0.8618  | 0.8944  |  0.8248  | 1.0000  | 0.8014  | 0.7353  |  0.9646   |  0.9341   | 0.9337  | 0.8389  | 0.9511  |    0.8891     |
+|     layoutlm-base     |  **0.9816**  | 0.8595  | 0.9597  |  0.8947  | 1.0000  | 0.8957  | 0.8948  |  0.9788   |  0.9338   | 0.9598  | 0.8633  | **0.9579**  |    0.9316     |
+|       bert-large      |  0.9286  | 0.8577  | 0.8650  |  0.8177  | 1.0000  | 0.7814  | 0.6960  |  0.9619   |  0.9284   | 0.9065  | 0.8320  | 0.9430  |    0.8765     |
+|     roberta-large     |  0.9479  | 0.8724  | 0.9081  |  0.8370  | 1.0000  | 0.8392  | 0.7451  |  0.9665   |  0.9334   | 0.9407  | 0.8494  | 0.9461  |    0.8988     |
+|     layoutlm-large    |  0.9784  | 0.8783  | 0.9556  |  0.8974  | **1.0000**  | 0.9146  | 0.9004  |  0.9790   |  0.9332   | 0.9596  | 0.8679  | 0.9552  |    0.9350     |
+|          X101         |  0.9717  | 0.8227  | 0.9435  |  0.8938  | 0.8812  | 0.9029  | 0.9051  |  0.9682   |  0.8798   | 0.9412  | 0.8353  | 0.9158  |    0.9051     |
+|  X101 & layoutlm-base |  0.9815  | 0.8907  | **0.9669**  |  0.9430  | 0.9990  | 0.9292  | **0.9300**  |  0.9843   |  **0.9437**   | 0.9664  | 0.8818  | 0.9575  |    0.9478     |
+| X101 & layoutlm-large |  0.9802  | **0.8964**  | 0.9666  |  **0.9440**  | 0.9994  | **0.9352**  | 0.9293  |  **0.9844**   |  0.9430   | **0.9670**  | **0.8875**  | 0.9531  |    **0.9488**     |
+
+
+
+We evaluate six models on the test set of DocBank. We notice that the LayoutLM gets the highest scores on the \{abstract, author, caption, equation, figure, footer, list, paragraph, section, table, title\} labels. The RoBERTa model gets the best performance on the "reference" label but the gap with the LayoutLM is very small. This indicates that the LayoutLM architecture is significantly better than the BERT and RoBERTa architecture in the document layout analysis task. 
+
+We also evaluate the ResNeXt-101 model and two ensemble models combining ResNeXt-101 and LayoutLM. The output of the ResNeXt-101 model is the bounding boxes of semantic structures. To unify the outputs of them, we mark the tokens inside each bounding box by the label of the corresponding bounding box. After that, we calculate the metrics following the above equation.
+
+## **Scripts**
+
+We provide a script to convert PDF files to the DocBank format data. You can run the PDF processing script pdf_process.py in the scripts directory. You may need to install some dependencies of this script through the pip package installer.
+
+~~~bash
+cd scripts
+python pdf_process.py   --data_dir /path/to/pdf/directory \
+                        --output_dir /path/to/data/output/directory
+~~~
+
+
+## **Paper and Citation**
+### DocBank: A Benchmark Dataset for Document Layout Analysis
+
+Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, Ming Zhou
+
+https://arxiv.org/abs/2006.01038
+```
+@misc{li2020docbank,
+    title={DocBank: A Benchmark Dataset for Document Layout Analysis},
+    author={Minghao Li and Yiheng Xu and Lei Cui and Shaohan Huang and Furu Wei and Zhoujun Li and Ming Zhou},
+    year={2020},
+    eprint={2006.01038},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```