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Indian Sign Language Recognition Using CNN-LSTM Architecture for Enhanced Gesture Prediction

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Anshara Beigh
Smriti Kumari
Rebekah Russel
Ali Imam Abidi
Published in
Computer Vision and Robotics
Publisher
Springer Nature Switzerland

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Abstract

This chapter explores the development of a CNN-Attention-LSTM model for Indian Sign Language (ISL) recognition, focusing on the extraction of spatial and temporal features from video data. The model combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks with an attention mechanism for temporal sequence analysis. Key topics include data preprocessing, model architecture, training and optimization, and performance evaluation. The results demonstrate that the proposed model achieves high accuracy and outperforms standalone CNN and LSTM architectures. The chapter also discusses the potential applications of the model in real-world scenarios, such as educational settings and workplace communication aids for the hearing-impaired community. Additionally, it highlights future research directions, including the integration of facial expression recognition and the optimization of the model for real-time deployment.
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previous chapter Music Recommendation System Based on Facial Emotion Recognition
next chapter A Modified Aggregation Operator and Score Function for Solving Multicriteria Decision Making Problem Under Neutrosophic Environment
Title
Indian Sign Language Recognition Using CNN-LSTM Architecture for Enhanced Gesture Prediction
Authors
Anshara Beigh
Smriti Kumari
Rebekah Russel
Ali Imam Abidi
Copyright Year
2026
Book
Computer Vision and Robotics

Print ISBN: 978-3-032-06252-9

Electronic ISBN: 978-3-032-06253-6

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-06253-6_16
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