# LayoutLM (Document Foundation Model) **Multimodal (text + layout/format + image) pre-training for [Document AI](https://www.microsoft.com/en-us/research/project/document-ai/)** - April, 2021: [LayoutXLM](https://github.com/microsoft/unilm/tree/master/layoutxlm) is coming by extending the LayoutLM into multilingual support! A multilingual form understanding benchmark [XFUND](https://github.com/doc-analysis/XFUND) is also introduced, which includes 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 Sep 27th, 2021: [**LayoutLM-cased**](https://huggingface.co/microsoft/layoutlm-base-cased) are on [HuggingFace](https://github.com/huggingface/transformers) \*\*\*\*\*** **\*\*\*\*\* 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. | name | #params | HuggingFace | | ----------------------- | ------- | ------------------------------------------------------------ | | LayoutLM-Base, Uncased | 113M | [Model Hub](https://huggingface.co/microsoft/layoutlm-base-uncased) | | LayoutLM-Base, Cased | 113M | [Model Hub](https://huggingface.co/microsoft/layoutlm-base-cased) | | LayoutLM-Large, Uncased | 343M | [Model Hub](https://huggingface.co/microsoft/layoutlm-large-uncased) | \*As some downstream datasets are the subsets of IIT-CDIP, we have carefully excluded the overlap portion from the pre-training data. ### Different Tokenizer Note that LayoutLM-Base-Cased requires a different tokenizer, based on RobertaTokenizer. You can initialize it as follows: ~~~ from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('microsoft/layoutlm-base-cased') ~~~ ## Fine-tuning Example on FUNSD ### Installation Please refer to [layoutlmft](../layoutlmft/README.md) ### Command ``` cd layoutlmft python -m torch.distributed.launch --nproc_per_node=4 examples/run_funsd.py \ --model_name_or_path microsoft/layoutlm-base-uncased \ --output_dir /tmp/test-ner \ --do_train \ --do_predict \ --max_steps 1000 \ --warmup_ratio 0.1 \ --fp16 ``` ### 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 @inproceedings{Xu2020LayoutLMPO, 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}, journal = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining}, year = {2020} } ``` ## 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`).