Review on visiosense: navigating the landscape of indian sign language detection and recognition

This paper introduces a system for instant recognition of Indian Sign Language (ISL) and gesture identification using grid-based features. Addressing communication barriers between hearing-impaired individuals and society, the system achieves high accuracy without external devices like gloves or Microsoft Kinect sensors. Leveraging face detection, object fixation, and skin color technologies for hand detection and tracking, the Laptop's camera captures ISL gestures. Grid-based feature extraction represents hand movements as feature vectors, classified through the k-nearest neighbor algorithm. Hand gesture classification employs hidden Markov models, achieving 99.7% accuracy for static tasks and 97.23% for orientation recognition.