Rasel, Annajiat AlimKarim, Dewan ZiaulRabbi, Kazi KamruzzamanDev, PrantoHossain, AkramSadman, Aninda2023-10-252023-10-2520222022-05ID 18101625ID 18101424ID 18101416ID 22141052http://hdl.handle.net/10361/21880Cataloged from PDF version of thesis.Includes bibliographical references (pages 45-46).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.When images of printed or handwritten are converted; be it mechanically or electronically to an editable text format, this is called optical character recognition. Bangla is one of the most complex languages as it has so many characters and digits. Moreover the Bangla language has about 300 composite characters. That is why the extraction of characters from images is more difficult for Bangla compared to other languages. Deep learning has recently developed good capabilities for extracting high-level features from an image kernel. This paper will propose a custom model KDANet and compare with some popular deep learning models that can recognize handwritten Bangla characters written in various and distinct handwriting styles. These systems learn more accurate and inclusive features from large-scale training datasets than earlier feature extraction techniques.46 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.Character recognitionBangla OCRKDANetComputer visionDeep learningConvolutional neural networksCognitive learning theoryNeural networks (Computer science)Thesis