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Indian Sign Language Alphabet Recognition Using Transfer Learning with MobileNetV2

Author

Listed:
  • Shalaka Gaikwad

    (Research Scholar, Department of Computer Science, Taywade College, Koradi)

  • Dr. Girish Katkar

    (Assistant Professor, Department of Computer Science, Taywade College, Koradi)

  • Dr. Ajay Ramteke

    (Assistant Professor, Department of Computer Science, Taywade College, Koradi)

Abstract

Indian Sign Language (ISL) recognition plays a vital role in bridging the communication gap between the hearing-impaired community and the general population. This research presents an efficient deep learning-based approach for static ISL alphabet recognition using transfer learning with MobileNetV2. A dataset consisting of 26,000 images representing 26 alphabet classes (A–Z) was used. The proposed model leverages a pre-trained MobileNetV2 backbone for feature extraction, followed by custom classification layers. Experimental results demonstrate a high validation accuracy of 99% and test accuracy 99.89%, indicating the effectiveness of the approach for real-world ISL recognition tasks.

Suggested Citation

  • Shalaka Gaikwad & Dr. Girish Katkar & Dr. Ajay Ramteke, 2026. "Indian Sign Language Alphabet Recognition Using Transfer Learning with MobileNetV2," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 11(2), pages 94-101, February.
    • Handle: RePEc:bjf:journl:v:11:y:2026:i:2:p:94-101
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