A curvilinear-based approach for sign-to-text conversion of Kannada deaf sign language

Abstract

This research addresses the challenge of translating Kannada sign language into text to improve communication for the deaf community. Existing methods, primarily shape-based approaches, often fail to accurately imprisonment the complexity of hand gestures, leading to reduced translation accuracy. This study proposes a curvilinear-based approach that leverages peak curvature features and contour evolution techniques to overcome these limitations. This method enhances the recognition and interpretation of sign language gestures while reducing processing overhead. Experimental results demonstrate that the proposed system significantly outperforms traditional methods, achieving higher precision and recall rates. The enhanced system provides a reliable solution for improving accessibility and communication for the deaf community. This research represents a significant step toward developing more inclusive digital communication tools, with future work focused on real-time processing and extending the system to other regional sign languages.