🤖 AI Summary
Low-quality invoice images—characterized by complex table structures, severe noise, and heterogeneous layouts—significantly degrade OCR accuracy. To address this, we propose an end-to-end OCR-driven pipeline for tabular data extraction. Our method introduces a dynamic image preprocessing mechanism to enhance readability of degraded invoices; designs an adaptive table boundary detection and row-column mapping algorithm to robustly localize non-standard tables and semantically align cells; and integrates Tesseract OCR with customized post-processing logic for accurate text recognition and structured reconstruction. Experiments on a real-world invoice dataset demonstrate substantial improvements: +12.7% in field-level accuracy and enhanced layout consistency. The pipeline enables high-precision financial automation and digital archival, exhibiting strong engineering deployability in production environments.
📝 Abstract
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.