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  • https://doi.org/10.1109/aisummit66170.2025.11410773Copy DOI Icon

Hand Landmark-Based Sign Language Recognition Using Random Forest and Support Vector Classification

  • Nov 19, 2025
  • Vijay A Sangolgi +3 more
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Abstract

Sign Language Detection project was just a step to real-time system that will be able to understand American Sign Language (ASL) hand gestures and convert them into a written language for the hearing-impaired community. This was a machine learning project that utilized the Random Forest and Support Vector Classifier (SVC) algorithms to obtain the highest accuracy for the classification of sign language gestures. Media Pipe hand landmarks can now be extracted, normalized, preprocessed and even the most important features of the gestures can be focused on. Ensemble learning which is a combination of the complementary strengths of the algorithms is one of the main reasons for the system to have robust classification. Besides the good performance and a variety of accuracy metrics such as precision, recall and confusion matrices, the system is also quite reliable in different gesture classes. The system with all its features such as real-time video processing and instant feedback can be used in educational, healthcare and social settings. The project will be the one that can bridge the communication gap existing between sign language users and the hearing world by going a step further in assisting the community in becoming more inclusive and accessible to the hearing-impaired."

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