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This paper presents GestureNet, a deep learning framework for the recognition of Indian Sign Language (ISL) gestures using five prominent convolutional neural network architectures: InceptionV3, ResNet50V2, InceptionResNetV2, VGG19, and MobileNetV2. The system aims to identify the most effective model for ISL recognition through performance metrics such as accuracy, precision, recall, and F1-score. Results show MobileNetV2 achieves the highest accuracy (99.14%), offering a lightweight and efficient solution for real-time applications. A Flask-based web interface allows users to select a model and upload images for gesture prediction.
Keywords:
Indian Sign Language, Gesture Recognition, Deep Learning, CNN, MobileNetV2, Flask UI
Cite Article:
"A Comparative Analysis of Deep Learning Models for Indian Sign Language Recognition", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 7, page no.a241-a244, July-2025, Available :http://www.ijrti.org/papers/IJRTI2507031.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator