An OCR-Based Intelligent System for Automated Invoice Data Extraction

Accurate and efficient extraction of invoice data is an essential requirement in modern business and financial operations, where organizations process large numbers of in- voices every day. Traditional manual invoice handling is time- consuming, labor-intensive, and highly error-prone. Conventional Optical Character Recognition (OCR) systems offer partial au- tomation by converting document images into machine-readable text, but they often fail to extract critical invoice fields reliably because of variations in layout, font style, image quality, scanning distortions, and background noise. These limitations lead to unstructured outputs and require extensive human verification. This paper presents an OCR-based intelligent system for auto- mated invoice data extraction by integrating YOLO-based object detection with selective OCR. Instead of applying OCR to the entire invoice image, the proposed system first detects important invoice fields such as invoice number, date, vendor details, and total amount. OCR is then applied only to the detected regions of interest, which reduces noise and improves recognition accuracy. The extracted text is further refined using rule-based validation and pattern matching to generate structured outputs in CSV and Excel-compatible form. The reformatted study reports an extraction accuracy of approximately 92–95% and a processing speed improvement of nearly 35–40% over a conventional OCR- only approach, demonstrating the practical value of combining region localization and targeted text recognition for invoice automation