用MobileNetV2和迁移学习实现印度手语实时识别，助力听障人士沟通
Real-time Sign Language Recognition Using MobileNetV2 and Transfer Learning
- 基于MobileNetV2和迁移学习构建轻量级手语识别模型
- 在自建数据集上实现92.3%的识别准确率，支持实时处理
- 专为印度手语设计，适合无障碍科技与残障辅助领域应用
印度听障群体亟需能帮助沟通的技术工具，但目前针对印度手语（ISL）的可用技术解决方案极为有限。尽管有大量ISL使用者，却因缺乏将手语信号高效转化为语音或文本的技术，难以参与社会与教育活动。我们发起此项目，响应日益增长的包容性科技需求，填补听障人士沟通障碍的空白。目标是利用卷积神经网络（CNN）构建可靠的符号语言识别系统。通过扩展沟通渠道，我们希望为印度听障人士带来更好的教育机会，并推动更包容的社会发展。
原文摘要 · Abstract (English)
The hearing-impaired community in India deserves the access to tools that help them communicate, however, there is limited known technology solutions that make use of Indian Sign Language (ISL) at present. Even though there are many ISL users, ISL cannot access social and education arenas because there is not yet an efficient technology to convert the ISL signal into speech or text. We initiated this initiative owing to the rising demand for products and technologies that are inclusive and help ISL, filling the gap of communication for the ones with hearing disability. Our goal is to build an reliable sign language recognition system with the help of Convolutional Neural Networks (CNN) to . By expanding communication access, we aspire toward better educational opportunities and a more inclusive society for hearing impaired people in India.
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