Reza, Md TanzimAlam, A.M. Esfar-ERakin, Shakeef AhmedRahman, Mohammed IntisharAhsan, Md.TahjidAlamgir, AfifMahmud, Md Sifat2025-01-062025-01-06©20232023-05ID 20301046ID 20301191ID 20301209ID 20301199ID 20101477http://hdl.handle.net/10361/25058Cataloged from PDF version of thesis.Includes bibliographical references (pages 52-53).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.Sign language is essential for effective communication among individuals with hearing or speech impairments. Automated recognition systems for sign language are crucial for facilitating learning and translation across different sign language variants. However, existing systems often struggle with high computational demands and large memory footprints, limiting their applicability in real-time and resourceconstrained environments. This research aims to develop an optimized pipeline for American Sign Language (ASL) recognition, focusing on the comparison between Binarized Neural Networks (BNNs) and traditional full-precision neural networks. Using Larq, an open-source Python library for training binarized models, we exploit BNNs’ advantages of reduced memory and computational needs, making them ideal for embedded systems and edge devices. The study utilizes a dataset of ASL alphabet images, encompassing a variety of hand configurations and movements. Data augmentation techniques are applied to address challenges like data imbalance and occlusions. Both binarized and traditional models are trained and optimized, and their performance is evaluated using metrics such as accuracy, precision, recall, F1-score, memory footprints and inference times. The results indicate that BNNs offer competitive performance with significantly lower computational requirements, paving the way for more efficient and accessible ASL recognition systems and demonstrating the potential of binarized models in this domain.52 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.Artificial intelligenceSign languageDeep learningAmerican sign languageBinaryDenseNet3ResNet50Sign language recognition.American Sign Language.Machine learning.Computer vision--Sign language recognition.Thesis