Downloads · 30 days
0
Sudhanshu1304/table-extraction
table-extraction is a machine learning model from Sudhanshu1304. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for PaddleOCR. The card lists the license as mit.
[](https://opensource.org/licenses/MIT) [](https://github.com/Sudhanshu1304/table-transformer) [](https://github.com/Sudhanshu1304/table-transformer/stargazers)
Downloads · 30 days
0
Access
Public
Updated Oct 14, 2025
Repo size
81.3 MB
Likes
2
Public
Click a slice to open those files.
.pt62.8 MB · 77%
From the Hugging Face model README
Table Transformer is an advanced open-source tool that leverages state-of-the-art OCR and computer vision techniques to extract structured tabular data from images. It is ideal for enhancing LLM preprocessing, powering data analysis pipelines, and automating your data extraction tasks.
Clone the Repository
Clone the repository to your local machine:
git clone https://github.com/Sudhanshu1304/table-transformer.git
cd table-transformer
Create and Activate Conda Environment
Create a new conda environment and activate it:
conda create --name myenv python=3.12.7
conda activate myenv
Install PaddlePaddle
Install PaddlePaddle in the conda environment:
python -m pip install paddlepaddle==3.0.0rc1 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
Install PaddleOCR
Install PaddleOCR:
pip install paddleocr
Install Additional Dependencies
Install other required packages:
pip install ultralytics pandas
pip install streamlit
project/
├── src/
│ ├── streamlit_app.py # Streamlit application
│ ├── table_creator/
│ │ └── processing.py # Core processing logic
│ ├── models/
│ │ └── text.py # table detection and text recognition
│
├── requirements.txt # Dependencies
├── README.md # Project documentation
└── .gitignore # Git ignore configuration
Run the Streamlit app to interact with the tool:
streamlit run src/streamlit_app.py
Contributions are welcome! Please fork the repository and submit a pull request with your improvements or new features.
This project is licensed under the MIT License.
Stay updated and connect for any queries or contributions:
If you find this tool useful, please consider giving it a ⭐ on GitHub. Your support is greatly appreciated!
Happy Extracting!