AI-Driven Indian Sign Language Recognition Using Hybrid CNN-BiLSTM Architecture for Divyangjan
Nithyanandh S
Dr. Nithyanandh S, Department of MCA, PSG College of Arts & Science, Coimbatore, (Tamil Nadu), India.
Manuscript received on 26 October 2025 | First Revised Manuscript received on 04 November 2025 | Second Revised Manuscript received on 20 November 2025 | Manuscript Accepted on 15 December 2025 | Manuscript published on 30 December 2025 | PP: 14-25 | Volume-14 Issue-1, December 2025 | Retrieval Number: 100.1/ijese.L262813121125 | DOI: 10.35940/ijese.L2628.14011225
Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Barriers in expressive and receptive communication for individuals with hearing and speech impairments remain a significant challenge to achieving equitable social interaction and digital accessibility. These limitations restrict their participation in everyday conversations, education, and professional environments, emphasizing the urgent need for intelligent assistive communication technologies. This research introduces an AI-driven real-time Indian Sign Language (ISL) recognition framework that integrates advanced computer vision and deep learning techniques to translate hand gestures into textual outputs. The primary objective of this study is to design a lightweight, accurate, and real-time sign language translator suitable for Divyangjan, ensuring inclusivity in educational and social interactions. The proposed research employs Mediapipe-based hand landmark extraction to detect 21 key points in each gesture frame, followed by preprocessing and normalisation to create robust spatial representations. A hybrid Convolutional Neural Network processes these landmark vectors through a Bidirectional Long Short-Term Memory (CNN–BiLSTM) model that captures both spatial and temporal dependencies in gesture motion, allowing it to recognise static and dynamic gestures such as “J” and “Z.” The system was trained on a self-collected dataset of over 26,000 gesture images covering all 26 ISL alphabets. Experimental analysis demonstrates that the proposed model achieved 97.8% accuracy, 97.4% precision, 97.2% recall, 97.3% F1-score, and a validation loss of only 0.08, outperforming traditional classifiers such as Random Forest and SVM by a significant margin. The trained model performs robustly under varying lighting, background, and hand orientation conditions, ensuring high reliability for real-world deployment. The novelty of this study lies in the fusion of Mediapipe landmark extraction with a temporal deep learning framework for continuous ISL gesture translation within an interactive CustomTkinter GUI. This human-centric, computationally efficient design enables accessible, real-time communication for the deaf and hard-of-hearing community, contributing to socially assistive AI systems that promote digital inclusivity and empowerment for Divyangjan.
Keywords: Artificial Intelligence, Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN), Gesture Recognition, Indian Sign Language (ISL).
Scope of the Article: Computer Vision