Comparative Analysis of Neural Network Models for Indian Sign Language Hand Gesture Recognition
- Title
- Comparative Analysis of Neural Network Models for Indian Sign Language Hand Gesture Recognition
- Creator
- Mullick, Tabassum; Gnana Prakasi, O.S.; Yogish, Deepa
- Description
- The recognition of sign language is a crucial element in filling communication gaps that exist in the population. As inclusive communication technologies become more popular, there has been a significant push to develop trustworthy systems for translating sign language into written or visual form. The use of hand gestures and body movements is a fundamental aspect of sign languages, which are commonly used by those who are deaf. The lack of proficiency in sign language makes communication difficult for most people. A project was undertaken to convert Indian Sign Language (ISL) into spoken language through research. The paper presents a comparison of various neural network models. Using OpenCVgenerated real-time images and MediaPipe, it is possible to identify hand movements and collect ISL gesture data in realtime. In the study, it was demonstrated that ResNet50 is 92 per cent accurate in real-time recognition when compared to other models. This work aims to promote inclusivity and communication skills among people who may not have the ability to hear or speak fluently. Adding face recognition to future work may improve accuracy and enable continuous sign language recognition, providing more dynamic and real-time translation capabilities. 2025 IEEE.
- Source
- Proceedings of 2025 IEEE International Conference on Contemporary Computing and Communications, InC4 2025;
- Date
- 01-01-2025
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- CNN; ISL; LSTM; MediaPipe; OpenCV; ResNet50; VGG16; YOLOv5
- Coverage
- Mullick T., Christ University, Bengaluru, India; Gnana Prakasi O.S., Christ University, Bengaluru, India; Yogish D., Christ University, Bengaluru, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISBN: 979-833152118-9;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Mullick, Tabassum; Gnana Prakasi, O.S.; Yogish, Deepa, “Comparative Analysis of Neural Network Models for Indian Sign Language Hand Gesture Recognition,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26158.