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

arXiv: https://arxiv.org/abs/2609.26088 · HTML: https://mlsanity.com/paper/2609.26088/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Builds on (most cited first, 2 of 2)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Interpretation of Swedish Sign Language using Convolutional Neural Networks and Transfer Learning](https://mlsanity.com/paper/2010.07827/) (2020)

## Abstract (arXiv)

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.