Title: Indian Sign Language Detection for Real-Time Translation using Machine Learning Show Chinese title

Title: 基于机器学习的实时翻译印度手语检测

Abstract: Gestural language is used by deaf & mute communities to communicate through hand gestures & body movements that rely on visual-spatial patterns known as sign languages. Sign languages, which rely on visual-spatial patterns of hand gestures & body movements, are the primary mode of communication for deaf & 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 & 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 & 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 & translation system built upon a Convolutional Neural Network (CNN). Our model is trained on a comprehensive ISL dataset & 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 & recall, ensuring its reliability for real-world applications. For real-time implementation, the framework integrates MediaPipe for precise hand tracking & motion detection, enabling seamless translation of dynamic gestures. This paper provides a detailed account of the model's architecture, the data preprocessing pipeline & the classification methodology. The research elaborates the model architecture, preprocessing & classification methodologies for enhancing communication in deaf & mute communities. Show Chinese abstract

Abstract: 手势语言被聋哑社区用于通过手部动作和身体运动进行交流，这些动作依赖于视觉空间模式，称为手语。 手语依赖于手部动作和身体运动的视觉空间模式，是全球聋哑社区的主要交流方式。 有效的交流是人类互动的基础，但这些社区的个体常常由于缺乏熟练的翻译人员和可访问的翻译技术而面临重大障碍。 本研究特别针对印度背景下的这些挑战，专注于印度手语（ISL）。 通过利用机器学习，本研究旨在弥合印度聋哑和听力障碍人群之间的关键交流差距，与全球其他手语相比，ISL的技术解决方案尚不成熟。 我们提出了一种基于卷积神经网络（CNN）的稳健、实时的ISL检测与翻译系统。 我们的模型在一个全面的ISL数据集上进行训练，并表现出卓越的性能，达到了99.95%的分类准确率。 这一高精度突显了模型识别不同手势细微视觉特征的能力。 该系统的有效性通过关键性能指标（包括准确率、F1分数、精确率和召回率）进行了严格评估，确保其在现实应用中的可靠性。 为了实现实时应用，该框架集成了MediaPipe以实现精确的手部追踪和运动检测，从而实现动态手势的无缝翻译。 本文详细描述了模型的架构、数据预处理流程和分类方法。 该研究详细阐述了模型架构、预处理和分类方法，以提高聋哑社区的交流效果。