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BDSLI: A hybrid CNN-Transformer model for Bengali Sign Language interpretation

2026-08-04 03:48:27
Abir Bin Yousuf, Muhammad Iqbal Hossain
arXiv_CV
       
Abstract

This study introduces a novel hybrid CNN-Transformer architecture to address the limited progress in Bengali SLR, focusing on isolated sign word recognition and sentence generation. This specific model combination is new to Bengali SLR tasks. A custom video dataset was developed, featuring 62 distinct Bengali sign words (250 samples/class), along with a separate test dataset. The CNN-Transformer model demonstrated superior performance against all comparative and baseline models (e.g., CNN-LSTM, standalone TCN), achieving a 99.58% training accuracy (99.48% validation) and a 98.65% test accuracy. The trained model was subsequently deployed in a web application for real-world validation.

Abstract (translated)

URL

https://arxiv.org/abs/2609.26088

PDF

https://arxiv.org/pdf/2609.26088.pdf

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