Sadeque, Farig YousufShihab, Eshtiak AlamAziz, Md Shamsur Shafi Nur EKarim, Kazi IsrarulSaad, TashfiaQais, Neelavro Shafin2025-06-242025-06-2420252025-02ID 21301502ID 21301432ID 21301509ID 21301320ID 21301501http://hdl.handle.net/10361/26278Cataloged from PDF version of thesis.Includes bibliographical references (pages 67-70).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025The need for effective sign language recognition and translation has become more critical to create a more inclusive society that addresses the communication concerns of the Deaf community. In recent years, the field has seen a revolutionary progress arc, spearheaded by the development of transformative Deep Learning based approaches such as reinforcement learning, spatio-temporal residual networks, temporal convolution modules, iterative alignment networks, and attention mechanisms. Yet, vision-based real time continuous sign language recognition (CSLR) continues to face several application challenges, encompassing its visual, sequential, and alignment modules. As such, we propose an end-to-end training model inspired by the recent successes of transfer learning and attention-based mechanisms in particular to achieve new state-of-the-art performance on current benchmarks. Our paper includes two variations of approaches to dealing with continuous sign language videos: a classification approach and a translation generation approach. It eventually highlights the suitability of the translation-based approach for this domain of research. A comparative analysis between Classification based and generation based model highlights the superior efficiency and accuracy of the latter, making it the most suitable model for real-time, sentence-level sign language-to-text translation. Furthermore, our optimized inference strategy significantly reduces latency, ensuring real-time translation speeds, which is a crucial requirement for practical applications in accessibility and assistive communication technologies.70 pagesenBRAC University theses reports 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.Sign language recognitionDeep learningTransformersVideo vision transformerSpatio-temporal residual networksData mining.Sign language--Real-time translation.Electric transformers.Thesis