# LayoutLM **Multimodal (text + layout/format + image) pre-training for document AI** - April 17th, 2021: [LayoutXLM](https://arxiv.org/abs/2104.08836) extends the LayoutLM/LayoutLMv2 into multilingual support! In addition, we also introduce XFUN, a multilingual form understanding benchmark including forms with human labeled key-value pairs in 7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese). - December 29th, 2020: [LayoutLMv2](https://arxiv.org/abs/2012.14740) is coming with the new SOTA on a wide varierty of document AI tasks, including [DocVQA](https://rrc.cvc.uab.es/?ch=17&com=evaluation&task=1) and [SROIE](https://rrc.cvc.uab.es/?ch=13&com=evaluation&task=3) leaderboard. ## Introduction LayoutLM is a simple but effective multi-modal pre-training method of text, layout and image for visually-rich document understanding and information extraction tasks, such as form understanding and receipt understanding. LayoutLM archives the SOTA results on multiple datasets. For more details, please refer to our paper: [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou, [KDD 2020](https://www.kdd.org/kdd2020/accepted-papers) [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou, [ACL 2021](#) [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei, [Preprint](#) ## Release Notes **\*\*\*\*\* New Aug 7th, 2020: Our new document understanding datasets, [TableBank](https://doc-analysis.github.io/tablebank-page/) (LREC 2020) and [DocBank](https://doc-analysis.github.io/docbank-page/) (COLING 2020), are now publicly available.\*\*\*\*\*** **\*\*\*\*\* New May 16th, 2020: Our LayoutLM paper has been accepted to KDD 2020 as a full paper in the research track\*\*\*\*\*** **\*\*\*\*\* New Feb 18th, 2020: Initial release of pre-trained models and fine-tuning code for LayoutLM v1 \*\*\*\*\*** ## Pre-trained Model We pre-train LayoutLM on IIT-CDIP Test Collection 1.0\* dataset with two settings. * LayoutLM-Base, Uncased (11M documents, 2 epochs): 12-layer, 768-hidden, 12-heads, 113M parameters || [OneDrive](https://1drv.ms/u/s!ApPZx_TWwibInS3JD3sZlPpQVZ2b?e=bbTfmM) | [Google Drive](https://drive.google.com/open?id=1Htp3vq8y2VRoTAwpHbwKM0lzZ2ByB8xM) * LayoutLM-Large, Uncased (11M documents, 2 epochs): 24-layer, 1024-hidden, 16-heads, 343M parameters || [OneDrive](https://1drv.ms/u/s!ApPZx_TWwibInSy2nj7YabBsTWNa?e=p4LQo1) | [Google Drive](https://drive.google.com/open?id=1tatUuWVuNUxsP02smZCbB5NspyGo7g2g) \*As some downstream datasets are the subsets of IIT-CDIP, we have carefully excluded the overlap portion from the pre-training data. ## Fine-tuning Example We evaluate LayoutLM on several document image understanding datasets, and it outperforms several SOTA pre-trained models and approaches. Setup environment as follows: ~~~bash conda create -n layoutlm python=3.6 conda activate layoutlm conda install pytorch==1.4.0 cudatoolkit=10.1 -c pytorch git clone https://github.com/NVIDIA/apex && cd apex pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./ pip install . ## For development mode # pip install -e ".[dev]" ~~~ ### Sequence Labeling Task We give a fine-tuning example for sequence labeling tasks. You can run this example on [FUNSD](https://guillaumejaume.github.io/FUNSD/), a dataset for document understanding tasks. First, we need to preprocess the JSON file into txt. You can run the preprocessing scripts `funsd_preprocess.py` in the `scripts` directory. For more options, please refer to the arguments. ~~~bash cd examples/seq_labeling ./preprocess.sh ~~~ After preprocessing, run LayoutLM as follows: ~~~bash python run_seq_labeling.py --data_dir data \ --model_type layoutlm \ --model_name_or_path path/to/pretrained/model/directory \ --do_lower_case \ --max_seq_length 512 \ --do_train \ --num_train_epochs 100.0 \ --logging_steps 10 \ --save_steps -1 \ --output_dir path/to/output/directory \ --labels data/labels.txt \ --per_gpu_train_batch_size 16 \ --per_gpu_eval_batch_size 16 \ --fp16 ~~~ Note: The `DataParallel` will be enabled automatically to utilize all GPUs. If you want to train with `DistributedDataParallel`, please run the script like: ~~~bash # Suppose you have 4 GPUs. python -m torch.distributed.launch --nproc_per_node=4 run_seq_labeling.py --data_dir data \ --model_type layoutlm \ --model_name_or_path path/to/pretrained/model/directory \ --do_lower_case \ --max_seq_length 512 \ --do_train \ --num_train_epochs 100.0 \ --logging_steps 10 \ --save_steps -1 \ --output_dir path/to/output/directory \ --labels data/labels.txt \ --per_gpu_train_batch_size 16 \ --per_gpu_eval_batch_size 16 \ --fp16 ~~~ Then you can do evaluation or inference by replacing `--do_train` with `--do_eval` or `--do_predict` Also, you can run Bert and RoBERTa baseline by modifying the `--model_type` argument. For more options, please refer to the arguments of `run.py`. ### Document Image Classification Task We also fine-tune LayoutLM on the document image classification task. You can download the [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset from [here](https://www.cs.cmu.edu/~aharley/rvl-cdip/). Because this dataset only provides the document image, you should use the OCR tool to get the texts and bounding boxes. For example, you can easily use Tesseract, an open-source OCR engine, to generate corresponding OCR data in hOCR format. For more details, please refer to the [Tesseract wiki](https://github.com/tesseract-ocr/tesseract/wiki). Your processed data should look like [this sample data](https://1drv.ms/u/s!ApPZx_TWwibInTlBa5q3tQ7QUdH_?e=UZLVFw). With the processed OCR data, you can run LayoutLM as follows: ~~~bash python run_classification.py --data_dir data \ --model_type layoutlm \ --model_name_or_path path/to/pretrained/model/directory \ --output_dir path/to/output/directory \ --do_lower_case \ --max_seq_length 512 \ --do_train \ --do_eval \ --num_train_epochs 40.0 \ --logging_steps 5000 \ --save_steps 5000 \ --per_gpu_train_batch_size 16 \ --per_gpu_eval_batch_size 16 \ --evaluate_during_training \ --fp16 ~~~ Similarly, you can do evaluation by changing `--do_train` to `--do_eval` and `--do_test` Like the sequence labeling task, you can run Bert and RoBERTa baseline by modifying the `--model_type` argument. ### Results #### SROIE (field-level) | Model | Hmean | | ------------------------------------------------------------ | ---------- | | BERT-Large | 90.99% | | RoBERTa-Large | 92.80% | | [Ranking 1st in SROIE](https://rrc.cvc.uab.es/?ch=13&com=evaluation&task=3) | 94.02% | | [**LayoutLM**](https://rrc.cvc.uab.es/?ch=13&com=evaluation&view=method_info&task=3&m=71448) | **96.04%** | #### RVL-CDIP | Model | Accuracy | | ------------------------------------------------------------ | ---------- | | BERT-Large | 89.92% | | RoBERTa-Large | 90.11% | | [VGG-16 (Afzal et al., 2017)](https://arxiv.org/abs/1704.03557) | 90.97% | | [Stacked CNN Ensemble (Das et al., 2018)](https://arxiv.org/abs/1801.09321) | 92.21% | | [LadderNet (Sarkhel & Nandi, 2019)](https://www.ijcai.org/Proceedings/2019/0466.pdf) | 92.77% | | [Multimodal Ensemble (Dauphinee et al., 2019)](https://arxiv.org/abs/1912.04376) | 93.07% | | **LayoutLM** | **94.42%** | #### FUNSD (field-level) | Model | Precision | Recall | F1 | | ------------- | ---------- | ---------- | ---------- | | BERT-Large | 0.6113 | 0.7085 | 0.6563 | | RoBERTa-Large | 0.6780 | 0.7391 | 0.7072 | | **LayoutLM** | **0.7677** | **0.8195** | **0.7927** | ## Citation If you find LayoutLM useful in your research, please cite the following paper: ``` latex @misc{xu2019layoutlm, title={LayoutLM: Pre-training of Text and Layout for Document Image Understanding}, author={Yiheng Xu and Minghao Li and Lei Cui and Shaohan Huang and Furu Wei and Ming Zhou}, year={2019}, eprint={1912.13318}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License This project is licensed under the license found in the LICENSE file in the root directory of this source tree. Portions of the source code are based on the [transformers](https://github.com/huggingface/transformers) project. [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct) ### Contact Information For help or issues using LayoutLM, please submit a GitHub issue. For other communications related to LayoutLM, please contact Lei Cui (`lecu@microsoft.com`), Furu Wei (`fuwei@microsoft.com`).