Automated Translation of Assamese Sign Language through Deep Learning and Computer Vision Techniques

DOI

10.2991/978-94-6463-858-5_42How to use a DOI?

Keywords

Assamese Sign Language; Computer Vision; Convolutional Neural Network; Hand Gesture Recognition

Abstract

This research introduces a system for recognizing Assamese Sign Language (ASLR) aimed at improving communication for individuals in the Assamese deaf community. The system utilizes advanced computer vision and deep learning methods to accurately identify and convert static hand gestures representing Assamese alphabets into readable text. A custom dataset was created, consisting of images of 10 selected Assamese alphabet signs (“অ”, “আ”, “ই”, “ঈ”, “উ”, “ও”, “এ”, “ক”, “জ”, “ল”), with image capture performed using OpenCV and hand landmarks detected through MediaPipe. A Convolutional Neural Network (CNN) was then trained to classify these gestures, achieving impressive accuracy with near-perfect precision and recall scores. The system seamlessly converts recognized letters into coherent Assamese words, enabling real-time translation. Issues related to the display of non-ASCII characters were addressed by employing Unicode-compatible fonts. The results of the experiments highlight the system’s effectiveness in recognizing sign language, offering significant potential for improving accessibility and communication technologies for people with hearing impairments. Future improvements will involve expanding the dataset to include dynamic gestures and complete sentences, as well as implementing the system for real-time use in mobile applications. This work contributes to the ongoing efforts to create an inclusive environment for individuals with hearing impairments in the Assamese speaking population.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Himangshu Chetia
AU  - Bidisha Bhuyan
AU  - Madhusmita Bhuyan
AU  - Chhaya Prasad
AU  - Chandana Dev
AU  - Golam Imran Hussain
PY  - 2025
DA  - 2025/11/04
TI  - Automated Translation of Assamese Sign Language through Deep Learning and Computer Vision Techniques
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 482
EP  - 492
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_42
DO  - 10.2991/978-94-6463-858-5_42
ID  - Chetia2025
ER  -