LayoutLM

Multimodal (text + layout/format + image) pre-training for document AI

April 17th, 2021: LayoutXLM 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 is coming with the new SOTA on a wide varierty of document AI tasks, including DocVQA and SROIE 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 Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou, KDD 2020

LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding 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 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 (LREC 2020) and DocBank (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 | Google Drive
LayoutLM-Large, Uncased (11M documents, 2 epochs): 24-layer, 1024-hidden, 16-heads, 343M parameters || OneDrive | Google Drive

*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:

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, 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.

cd examples/seq_labeling
./preprocess.sh

After preprocessing, run LayoutLM as follows:

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:

# 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 dataset from here. 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. Your processed data should look like this sample data.

With the processed OCR data, you can run LayoutLM as follows:

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	94.02%
LayoutLM	96.04%

RVL-CDIP

Model	Accuracy
BERT-Large	89.92%
RoBERTa-Large	90.11%
VGG-16 (Afzal et al., 2017)	90.97%
Stacked CNN Ensemble (Das et al., 2018)	92.21%
LadderNet (Sarkhel & Nandi, 2019)	92.77%
Multimodal Ensemble (Dauphinee et al., 2019)	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:

@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 project. Microsoft Open Source Code of Conduct

Contact Information

For help or issues using LayoutLM, please submit a GitHub issue.

For other communications related to LayoutLM, please contact Lei Cui ([email protected]), Furu Wei ([email protected]).