Implementation of DocFormer: End-to-End Transformer for Document Understanding, 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.
I tried to pre-train DocFormer on the task of MLM on a subset of IDL Dataset. The weights are here, and the associated kaggle notebook for fine-tuning on FUNSD is attached here
There might be some issues with the import of pytessaract, so in order to debug that, we need to write
pip install pytesseract
sudo apt install tesseract-ocrAnd then,
!git clone https://github.com/shabie/docformer.git
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)MIT
@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}
}