{"ID":2890014,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.20414","arxiv_id":"2507.20414","title":"Indian Sign Language Detection for Real-Time Translation using Machine Learning","abstract":"Gestural language is used by deaf \u0026 mute communities to communicate through hand gestures \u0026 body movements that rely on visual-spatial patterns known as sign languages. Sign languages, which rely on visual-spatial patterns of hand gestures \u0026 body movements, are the primary mode of communication for deaf \u0026 mute communities worldwide. Effective communication is fundamental to human interaction, yet individuals in these communities often face significant barriers due to a scarcity of skilled interpreters \u0026 accessible translation technologies. This research specifically addresses these challenges within the Indian context by focusing on Indian Sign Language (ISL). By leveraging machine learning, this study aims to bridge the critical communication gap for the deaf \u0026 hard-of-hearing population in India, where technological solutions for ISL are less developed compared to other global sign languages. We propose a robust, real-time ISL detection \u0026 translation system built upon a Convolutional Neural Network (CNN). Our model is trained on a comprehensive ISL dataset \u0026 demonstrates exceptional performance, achieving a classification accuracy of 99.95%. This high precision underscores the model's capability to discern the nuanced visual features of different signs. The system's effectiveness is rigorously evaluated using key performance metrics, including accuracy, F1 score, precision \u0026 recall, ensuring its reliability for real-world applications. For real-time implementation, the framework integrates MediaPipe for precise hand tracking \u0026 motion detection, enabling seamless translation of dynamic gestures. This paper provides a detailed account of the model's architecture, the data preprocessing pipeline \u0026 the classification methodology. The research elaborates the model architecture, preprocessing \u0026 classification methodologies for enhancing communication in deaf \u0026 mute communities.","short_abstract":"Gestural language is used by deaf \u0026 mute communities to communicate through hand gestures \u0026 body movements that rely on visual-spatial patterns known as sign languages. Sign languages, which rely on visual-spatial patterns of hand gestures \u0026 body movements, are the primary mode of communication for deaf \u0026 mute communit...","url_abs":"https://arxiv.org/abs/2507.20414","url_pdf":"https://arxiv.org/pdf/2507.20414v2","authors":"[\"Rajat Singhal\",\"Jatin Gupta\",\"Akhil Sharma\",\"Anushka Gupta\",\"Navya Sharma\"]","published":"2025-07-27T21:15:46Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}