Table Transformer

Abstract

1. Introduction

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.

2. Objective

🔍 Table Extraction Tool: A powerful open-source solution combining OCR and computer vision for extracting structured tabular data from images. Ideal for LLM preprocessing, data analysis, and automation. 🚀

This project demonstrates how Python, Computer Vision, Hugging Face can be applied to a real-world Data Science problem.

3. Key Features / Modules

• Automatic Table Detection: Effortlessly detect tables in images.
• OCR-based Document Processing: Extract text with high accuracy.
• Integrated Models: Seamlessly combine OCR and table detection models.
• Flexible Export Options: Export data as DataFrame, HTML, CSV, and more.

4. Technology Stack

• PaddleOCR: For text extraction.
• Hugging Face Table Detection: For table structure detection.

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Sudhanshu1304/table-transformer.git
cd table-transformer

1. Clone the Repository
2. Create and Activate Conda Environment
3. Install PaddlePaddle
4. Install PaddleOCR
5. Install Additional Dependencies

git clone https://github.com/Sudhanshu1304/table-transformer.git
   cd table-transformer

conda create --name myenv python=3.12.7
   conda activate myenv

python -m pip install paddlepaddle==3.0.0rc1 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/

pip install paddleocr

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by Sudhanshu1304 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.