# Real Time Bangladeshi Sign Language Detection using Faster R-CNN

arXiv: https://arxiv.org/abs/1811.12813 · HTML: https://mlsanity.com/paper/1811.12813/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2018.

## Built on by (most cited first, 6 of 6)

- [BdSL36: A Dataset for Bangladeshi Sign Letters Recognition](https://mlsanity.com/paper/2110.00869/) (2021)
- [Bangla sign language recognition using concatenated BdSL network](https://mlsanity.com/paper/2107.11818/) (2021)
- [Bengali Sign Language Recognition through Hand Pose Estimation using Multi-Branch Spatial-Temporal Attention Model](https://mlsanity.com/paper/2408.14111/) (2024)
- [PerSign: Personalized Bangladeshi Sign Letters Synthesis](https://mlsanity.com/paper/2209.14591/) (2022)
- [BdSpell: A YOLO-based Real-time Finger Spelling System for Bangla Sign Language](https://mlsanity.com/paper/2309.13676/) (2023)
- [Real-time Bangla Sign Language Translator](https://mlsanity.com/paper/2412.16497/) (2024)

## Abstract (arXiv)

Bangladeshi Sign Language (BdSL) is a commonly used medium of communication for the hearing-impaired people in Bangladesh. Developing a real time system to detect these signs from images is a great challenge. In this paper, we present a technique to detect BdSL from images that performs in real time. Our method uses Convolutional Neural Network based object detection technique to detect the presence of signs in the image region and to recognize its class. For this purpose, we adopted Faster Region-based Convolutional Network approach and developed a dataset - BdSLImset - to train our system. Previous research works in detecting BdSL generally depend on external devices while most of the other vision-based techniques do not perform efficiently in real time. Our approach, however, is free from such limitations and the experimental results demonstrate that the proposed method successfully identifies and recognizes Bangladeshi signs in real time.