This paper presents the design and development of an OCR-powered pipeline for efficient table extraction from invoices. The system leverages Tesseract OCR for text recognition and custom post-processing logic to detect, align, and extract structured tabular data from scanned invoice documents. Our approach includes dynamic preprocessing, table boundary detection, and row-column mapping, optimized for noisy and non-standard invoice formats. The resulting pipeline significantly improves data extraction accuracy and consistency, supporting real-world use cases such as automated financial workflows and digital archiving.
@article{arxiv.2507.07029,
title = {Design and Implementation of an OCR-Powered Pipeline for Table Extraction from Invoices},
author = {Parshva Dhilankumar Patel},
journal= {arXiv preprint arXiv:2507.07029},
year = {2025}
}
Comments
17 pages, 23 figures, submitted to arXiv in July 2025