Document Digitization Using Optical Character Recognition - A Case Study of Mark Entry Automation in Educational Institutions

This paper analyses and provides solutions to al-leviate the challenge of manual mark entry in educational institutions by introducing a user-friendly software tool that combines Optical Character Recognition (OCR) technology and advanced artificial intelligence (AI). This tool allows educators to capture images of handwritten digits on answer scripts using a camera, converting them into a CSV file for efficient data management. Leveraging cutting-edge frameworks like Tensor-Flow and a custom-built Convolutional Neural Network (CNN) model, the results indicate that the project achieves precise digit recognition and outperforms existing solutions. Beyond education, this modular system holds potential for broader applications in streamlining data handling and reducing manual labor in various domains.