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https://github.com/shabie/docformer
Implementation of DocFormer: End-to-End Transformer for Document Understanding, a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU)
https://github.com/shabie/docformer
Last synced: 2 days ago
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Implementation of DocFormer: End-to-End Transformer for Document Understanding, a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU)
- Host: GitHub
- URL: https://github.com/shabie/docformer
- Owner: shabie
- License: mit
- Created: 2021-09-24T06:29:55.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2023-02-13T07:36:08.000Z (over 1 year ago)
- Last Synced: 2024-08-02T13:27:52.117Z (3 months ago)
- Language: Python
- Homepage:
- Size: 5.04 MB
- Stars: 251
- Watchers: 15
- Forks: 40
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DocFormer - PyTorch
Implementation of [DocFormer: End-to-End Transformer for Document Understanding](https://arxiv.org/abs/2106.11539), a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU) 📄📄📄.
DocFormer is a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU). In addition, DocFormer is pre-trained in an unsupervised fashion using carefully designed tasks which encourage multi-modal interaction. DocFormer uses text, vision and spatial features and combines them using a novel multi-modal self-attention layer. DocFormer also shares learned spatial embeddings across modalities which makes it easy for the model to correlate text to visual tokens and vice versa. DocFormer is evaluated on 4 different datasets each with strong baselines. DocFormer achieves state-of-the-art results on all of them, sometimes beating models 4x its size (in no. of parameters).
The official implementation was not released by the authors.
## NOTE:
I tried to pre-train DocFormer on the task of MLM on a subset of [IDL Dataset](https://github.com/furkanbiten/idl_data). The weights are [here](https://www.kaggle.com/code/akarshu121/downloading-docformer-weights), and the associated kaggle notebook for fine-tuning on FUNSD is attached [here](https://www.kaggle.com/code/akarshu121/ckpt-docformer-for-token-classification-on-funsd/notebook?scriptVersionId=118952199)
## Install
There might be some issues with the import of pytessaract, so in order to debug that, we need to write
```python
pip install pytesseract
sudo apt install tesseract-ocr
```
And then,
```python
!git clone https://github.com/shabie/docformer.git
```
## Usage
```python
import sys
sys.path.extend(['docformer/src/docformer/'])
import modeling, dataset
from transformers import BertTokenizerFast
config = {
"coordinate_size": 96,
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"image_feature_pool_shape": [7, 7, 256],
"intermediate_ff_size_factor": 4,
"max_2d_position_embeddings": 1000,
"max_position_embeddings": 512,
"max_relative_positions": 8,
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"shape_size": 96,
"vocab_size": 30522,
"layer_norm_eps": 1e-12,
}
fp = "filepath/to/the/image.tif"
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
encoding = dataset.create_features(fp, tokenizer, add_batch_dim=True)
feature_extractor = modeling.ExtractFeatures(config)
docformer = modeling.DocFormerEncoder(config)
v_bar, t_bar, v_bar_s, t_bar_s = feature_extractor(encoding)
output = docformer(v_bar, t_bar, v_bar_s, t_bar_s) # shape (1, 512, 768)
```
## License
MIT
## Maintainers
- [uakarsh](https://github.com/uakarsh)
- [shabie](https://github.com/shabie)
## Contribute
## Citations
```bibtex
@InProceedings{Appalaraju_2021_ICCV,
author = {Appalaraju, Srikar and Jasani, Bhavan and Kota, Bhargava Urala and Xie, Yusheng and Manmatha, R.},
title = {DocFormer: End-to-End Transformer for Document Understanding},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {993-1003}
}
```