Design and Implementation of an OCR-Powered Pipeline for Table Extraction from Invoices

This paper presents a robust system for automated invoice data extraction using a hybrid pipeline that combines OpenCV-based pre-processing with OCR and advanced table extraction techniques. Our approach addresses real-world challenges including skewed perspectives, variable lighting, noise from signatures, barcodes, staplers, and broken table structures. We segment invoices into detail and product sections, apply hybrid table detection using both Img2Table and manual fallback methods, and finally generate structured JSON outputs using row-wise OCR. This method proves particularly effective for physical invoices with multiple products and complex layouts, significantly reducing the need for manual data entry.