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]"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.shAfter 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 \
--fp16Import the module custom_preprocess.py
Pass the image to custom_img_annotation_.write_annoteFile() for preprocessing the new image.
Calling custom_img_annotation_.convert() and custom_img_annotation_.seg() will proce the test.txt file required by layoutLm model for prediction.
After the preprocessing, run layoutlm using --dopredict method as follows.
python run_seq_labeling.py --do_predict \
--data_dir data \
--model_type layoutlm \
--model_name_or_path output \
--do_lower_case \
--output_dir output \
--labels data/labels.txt \
--fp16| Model | Hmean |
|---|---|
| BERT-Large | 90.99% |
| RoBERTa-Large | 92.80% |
| Ranking 1st in SROIE | 94.02% |
| LayoutLM | 96.04% |
| 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% |
| 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 |
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}
}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
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]).
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