Bangla Braille Character Recognition using Convolutional Neural Network

Abstract

Through the creation of a Convolutional Neural Network (CNN)-based model that is inspired by the YOLO architecture, the purpose of this thesis is to find a solution to the substantial difficulty of Braille character recognition. The purpose of this research is to prioritize accessibility for those who are visually challenged and rely on Braille for communication and education. Specifically, the research is designed to transform Bangla letters from Braille patterns. For the purpose of ensuring that the training and testing stages are robust, a dataset that was painstakingly selected and specially designed for this particular task was painstakingly collected and annotated. However, the CNN model had difficulty reliably identifying contextual characters within sentences or phrases, despite the fact that it was able to recognize individual Bangla characters within Braille patterns with exceptional competence. The study highlights the important need to improve the accuracy and durability of such models in order to attain practical utility in applications that are based in the real world. For the purpose of model training, the system makes use of Google Colab Pro. Additionally, it makes use of advanced GPU capabilities and makes use of TensorFlow and Keras libraries for efficient implementation. In the future, efforts will be focused on refining the model in order to increase contextual character identification. The ultimate objective is to broaden the range of educational options available to visually impaired people and to improve the quality of life for those individuals who rely on Braille as an essential form of communication and literacy support. By tackling these problems, our research makes a contribution to the advancement of accessible technology and aids the inclusion of visually impaired individuals in educational settings and in day-to-day life.