Alam, Md. AshrafulFairuz, Fabliha AktherRoy, SantonuMahmud, SajidTasnim, SumiyaRafey, Inkiad Bin Ershad2026-09-132026-09-1320262026-06ID 22101200ID 21201475ID 20101076ID 21201518ID 21201516https://hdl.handle.net/10361/29882This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2026.Cataloged from PDF version of thesis.Includes bibliographical references (pages 61-64).Deaf and hard-of-hearing communities in Bangladesh face a persistent communication gap: existing sign language recognition systems process hand gestures but ignore the facial expressions and head movements that carry grammatical meaning in Bangladeshi Sign Language (BdSL). This study introduces BdSL-NMM, the first BdSL dataset annotated with non-manual marker labels alongside word-level glosses, covering 62 sign classes and 5 expression categories across 4,170 recordings from seven native signers. To evaluate on this dataset, a multi-stream Transformer architecture called SignNet-V2 was developed, which simultaneously recognises sign words and non-manual expression markers from skeletal landmark sequences. The system processes four input modalities, namely body pose, left hand, right hand, and face mesh, through dedicated stream-specific encoders, cross-stream attention fusion, hierarchical temporal encoding, and a multi-task classification head. All models were trained and evaluated under leave-one-signer-out cross-validation, where the test signer never appeared during training, providing a realistic measure of cross-signer generalisation. Under this protocol, SignNet-V2 achieves 38.44% top-1 word recognition accuracy. For expression recognition, a 56-dimensional framework of normalised geometric and temporal features achieves 66.39% overall accuracy under signer-independent evaluation, with neutral and negation recall of 97.22% and 91.18% on the unseen test signer. A second interpretation stage passes recognised signs and expression tags to Google Gemini, which generates grammatically correct Bengali sentences. This work establishes the first published benchmarks for simultaneous word and non-manual marker recognition in BdSL under signer-independent evaluation.64 pagesen-USAttribution-NonCommercial-NoDerivatives 4.0 InternationalBRAC 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.http://creativecommons.org/licenses/by-nc-nd/4.0/Sign language recognitionTransformerLarge language modelNatural language processingDeep learningSign Language--Machine translating.Electric transformers.Natural language processing (Computer science).Neural networks (Computer science).Thesis