Rasel, Annajiat AlimKamal, Rizvy Ahmed2024-10-212024-10-21©20232023ID 23141083http://hdl.handle.net/10361/24364Cataloged from PDF version of project.Includes bibliographical references (page 29).This project is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.Optical Character Recognition (OCR) technology has made an excellent stride in recent years, yet the accurate digitization of Bengali handwritten script remains a formidable challenge. This project introduces ’Bengali CharNet’, an improved deep learning-based model, specifically designed to advance OCR capabilities for Bengali handwriting, which is notably intricate and diverse in its character composition. The project aims to fill a crucial gap in OCR technology’s effectiveness with complex scripts like Bengali, which is the seventh most-spoken language in the world. The results of this research project are significant, with Bengali CharNet demonstrating a remarkable improvement in accuracy, precision, and recall compared to existing OCR models. The model achieved an overall accuracy of 96.8%, showcasing its effectiveness in recognizing and digitizing Bengali handwritten characters. This achievement represents a substantial advancement in the field of OCR, particularly for scripts that possess a high degree of complexity.29 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Bangla OCRCNNBengali handwritten charactersBengali CharNetOptical character recognition.Bengali language--Text processing.Machine learning.Artificial intelligence.Project Report