This content originally appeared on DEV Community and was authored by Sudhanshu
Extracting tabular data from documents remains one of the biggest challenges in industries like healthcare, insurance, and finance. When processing claims, invoices, or contracts, maintaining the structure of complex tables is crucial for accurate insights.
Traditional methods — such as OCR paired with Language Models — often lose the structural integrity of tables, leading to mismatched columns and rows. Vision-based LLMs promise better accuracy but come with significant computational costs and occasional hallucinations.
I’m excited to share a cost-effective and scalable open-source solution that addresses these challenges!
🛠️ What Does the Tool Do?
My solution is designed to extract structured tabular data from document images, combining the best of OCR and computer vision technologies with custom processing logic.
Here’s how it works:
Table Detection: Identifies and extracts tables from images using HuggingFace’s Table Detection.
OCR Integration: Uses PaddleOCR to read text within table cells.
Linked List Algorithm: Builds a structured linked list to preserve the table layout and outputs it in multiple formats like Pandas DataFrames, HTML tables, or CSVs.
🔍 Why Is This Important?
Maintains Structural Integrity: The tool ensures tables retain their format, significantly improving downstream processing accuracy.
Adaptable to Complex Cases: It can handle basic to moderately complex tables and provides a foundation for applying custom post-processing logic.
Cost-Effective: Unlike Vision LLMs, this solution uses lightweight open-source tools, making it highly affordable and efficient.
💡 How Can You Use It?
Directly use the structured output for simple workflows.
Feed the output into an LLM to improve the accuracy of information extraction, as the structural context is retained.
Replace the open-source components (e.g., PaddleOCR) with advanced tools for higher precision.
🔗 Get Started Today
This project is completely open-source and available on GitHub! It’s easy to set up and comes with detailed instructions for implementation.
👉 Explore the Repository on GitHub
If you’re looking for a scalable, reliable, and accurate solution to extract tabular data from documents, this tool is for you. Let me know your thoughts, and feel free to contribute to the project!
This content originally appeared on DEV Community and was authored by Sudhanshu
Sudhanshu | Sciencx (2025-01-24T19:19:18+00:00) Revolutionising Table Extraction: Simplifying Document Processing (Open Source). Retrieved from https://www.scien.cx/2025/01/24/revolutionising-table-extraction-simplifying-document-processing-open-source/
Please log in to upload a file.
There are no updates yet.
Click the Upload button above to add an update.