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Real-time sign language recognition depends on an enhanced CNN-LSTM architecture which uses ASL training data. A new preprocessing approach boosts image resolution to 46 × 46 pixels thus enhancing the recognition precision. Numerous frames enter the system which enables a deep learning model to analyze spatial and temporal features to identify different hand signals in real time. The system integrates with the Flask-React-based frontend which allows real-time predictions through webcam interfaces in order to support practical field use. The system translates acknowledged signs into speech audio through Text-to-Speech APIs which drives inclusion between hearing-impaired users and people who do not use sign language. The system’s effectiveness was validated through experimental testing which reached 95% accuracy. The system’s development will advance by implementing support for local sign languages with mobile platform integration.

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American Sign Language Recognition Using Hybrid Deep Learning Architecture

  • 会议论文

  • 发表日期：

    2026年

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  • Abhijeet Kumar,
  • Yash Shekhawat,
  • Rahul Kumar,
  • K. Naresh

摘要

Real-time sign language recognition depends on an enhanced CNN-LSTM architecture which uses ASL training data. A new preprocessing approach boosts image resolution to 46 × 46 pixels thus enhancing the recognition precision. Numerous frames enter the system which enables a deep learning model to analyze spatial and temporal features to identify different hand signals in real time. The system integrates with the Flask-React-based frontend which allows real-time predictions through webcam interfaces in order to support practical field use. The system translates acknowledged signs into speech audio through Text-to-Speech APIs which drives inclusion between hearing-impaired users and people who do not use sign language. The system’s effectiveness was validated through experimental testing which reached 95% accuracy. The system’s development will advance by implementing support for local sign languages with mobile platform integration.