Awesome-Table-Recognition

Original Code is Lore I modified this code with faster inference and higher accuracy by removing locations regression and add a simple and efficient postprocess

Install

export PATH="/usr/local/cuda-11.8/bin:$PATH"
export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
source ~/.bashrc
conda create --name Lore python=3.7
conda activate Lore
pip install -r requirements.txt
pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
chmod +x  *.sh
cd lib/models/networsk/DCNv2
python3 setup.py build develop

Note: if It causes error : Unknown CUDA arch (8.9,9.0,...) or GPU not supported --> export TORCH_CUDA_ARCH_LIST=7.5 or export TORCH_CUDA_ARCH_LIST=6.0 .The rerun setup.py

Weights

Available model weights (using dla-34 backbone):

Name	Backbone	Regressor Arc	Image Size	Checkpoint
ckpt_wtw	DLA-34	4+4	1024	Trained on WTW
ckpt_ptn	DLA-34	3+3	512	Trained on PubTabNet
ckpt_wireless	ResNet-18	4+4	768	Trained on Wireless Tables*

Inference

python inference.py

Result

Table Sample

Result Sample

Table Detection

Datset TableBank and WTW

Availabel Dataset for training Yolo in Here