Document Digitization

Abstract

This project presents a smart and efficient system for document digitization and data entry automation using a combination of Optical Character Recognition (OCR) and deep learning techniques. The proposed system primarily targets the digitization of loan application forms, which are often received in printed or handwritten formats. Using Tesseract OCR, the system extracts text from scanned images, followed by preprocessing techniques such as grayscale conversion and Otsu thresholding to enhance image clarity.
To improve accuracy, transformer-based models like BERT or T5 are incorporated for contextual text understanding and refinement. Key fields such as names, dates, and account numbers are identified and structured using Named Entity Recognition (NER) and regex-based validation. The extracted and cleaned data is then exported into Excel files in a fixed format, making it easy to review and integrate with enterprise applications.
This solution significantly reduces human effort, eliminates common manual errors, supports real-time processing, and ensures scalability for large-scale document handling.