# BdSLW60: A Word-Level Bangla Sign Language Dataset

arXiv: https://arxiv.org/abs/2402.08635 · HTML: https://mlsanity.com/paper/2402.08635/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 61 references that are in the map.
- First posted on arXiv 13 Feb 2024.

## Builds on (most cited first, 13 of 13)

- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [Improving Sign Language Translation with Monolingual Data by Sign Back-Translation](https://mlsanity.com/paper/2105.12397/) (2021)
- [Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution](https://mlsanity.com/paper/2002.11616/) (2020)
- [AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods](https://mlsanity.com/paper/2008.00932/) (2020)
- [Ham2Pose: Animating Sign Language Notation into Pose Sequences](https://mlsanity.com/paper/2211.13613/) (2022)
- [ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research](https://mlsanity.com/paper/2105.05066/) (2021)
- [Slovo: Russian Sign Language Dataset](https://mlsanity.com/paper/2305.14527/) (2023)
- [BdSL36: A Dataset for Bangladeshi Sign Letters Recognition](https://mlsanity.com/paper/2110.00869/) (2021)
- [A Deep Learning-based Multimodal Depth-Aware Dynamic Hand Gesture Recognition System](https://mlsanity.com/paper/2107.02543/) (2021)
- [Word-level Sign Language Recognition with Multi-stream Neural Networks Focusing on Local Regions and Skeletal Information](https://mlsanity.com/paper/2106.15989/) (2021)
- [BDSL 49: A Comprehensive Dataset of Bangla Sign Language](https://mlsanity.com/paper/2208.06827/) (2022)
- [ISLTranslate: Dataset for Translating Indian Sign Language](https://mlsanity.com/paper/2307.05440/) (2023)

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

- [BdSLW401: Transformer-Based Word-Level Bangla Sign Language Recognition Using Relative Quantization Encoding (RQE)](https://mlsanity.com/paper/2503.02360/) (2025)
- [Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks](https://mlsanity.com/paper/2506.04367/) (2025)
- [Prompting with Sign Parameters for Low-resource Sign Language Instruction Generation](https://mlsanity.com/paper/2508.16076/) (2025)
- [Bangla Sign Language Translation: Dataset Creation Challenges, Benchmarking and Prospects](https://mlsanity.com/paper/2511.21533/) (2025)
- [BdSL-SPOTER: A Transformer-Based Framework for Bengali Sign Language Recognition with Cultural Adaptation](https://mlsanity.com/paper/2511.12103/) (2025)
- [Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards](https://mlsanity.com/paper/2606.19352/) (2026)

## Abstract (arXiv)

Sign language discourse is an essential mode of daily communication for the deaf and hard-of-hearing people. However, research on Bangla Sign Language (BdSL) faces notable limitations, primarily due to the lack of datasets. Recognizing wordlevel signs in BdSL (WL-BdSL) presents a multitude of challenges, including the need for well-annotated datasets, capturing the dynamic nature of sign gestures from facial or hand landmarks, developing suitable machine learning or deep learning-based models with substantial video samples, and so on. In this paper, we address these challenges by creating a comprehensive BdSL word-level dataset named BdSLW60 in an unconstrained and natural setting, allowing positional and temporal variations and allowing sign users to change hand dominance freely. The dataset encompasses 60 Bangla sign words, with a significant scale of 9307 video trials provided by 18 signers under the supervision of a sign language professional. The dataset was rigorously annotated and cross-checked by 60 annotators. We also introduced a unique approach of a relative quantization-based key frame encoding technique for landmark based sign gesture recognition. We report the benchmarking of our BdSLW60 dataset using the Support Vector Machine (SVM) with testing accuracy up to 67.6% and an attention-based bi-LSTM with testing accuracy up to 75.1%. The dataset is available at https://www.kaggle.com/datasets/hasaniut/bdslw60 and the code base is accessible from https://github.com/hasanssl/BdSLW60_Code.