Published Paper Details:

Sign language is a visual language that uses hand gestures to convey meaning, primarily used by the deaf and hearing-impaired community. Sign language recognition systems aim to bridge the communication gap between individuals who use sign language and the wider hearing population by translating sign language gestures into written or spoken language. Existing systems rely on a range of techniques including computer vision, machine learning models such as convolutional neural networks (CNNs), and traditional methods like support vector machines (SVMs). However, these existing models face limitations in recognizing a broad vocabulary and complex grammatical structures of sign languages, often leading to ambiguity and misinterpretation. The proposed system addresses these challenges by expanding the model to include more signs, resulting in a more effective framework for real-time applications. By integrating advanced computer vision techniques and machine learning algorithms, the system can recognize hand gestures based on features such as orientation, centroid, and finger positions. Leveraging a comprehensive dataset for diverse signing styles enhances generalization and fosters more effective communication for individuals with hearing impairments.