OCR-Driven Automation: A Case Study on Document Processing Using Tesseract and OpenCV
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Abstract
The research related to OCR-driven automation shows how merging Tesseract OCR and OpenCV can automatically process and improve documents. When image pre-processing is used, OCR does better in handling formats such as invoices and reports. Studies revealed that businesses managed to handle document challenges more easily, quickly and correctly by automation processes supported by OCR. It examines the recent growth in Optical Character Recognition (OCR), mainly looking at Tesseract and OpenCV for applying document automation in different industries.
It discusses deep learning, methods for preparing data and examples from retail, logistics and manufacturing. Through secondary analysis and case studies, the paper demonstrates better results in accuracy, processing speed and inclusion. Comparing different systems points out their main pros and cons, leading to improved ideas and progress in OCR packages. It also finds that preprocessing images makes the system capable of achieving very high accuracy in recognising text. Tesseract is reliable, and it is suggested to include AI models in future versions to improve its handling of difficult or poor-quality documents.