Scholarlink

The World Health Organization states that over 5% of the global population—approximately 430 million people—experience some degree of deafness and muteness. The lack of sign language proficiency among the general population creates communication barriers, impacting the emotional well-being of both deaf-mute and hearing individuals. Developing accessible tools for self-guided sign language learning is crucial for fostering mental health and mutual understanding across diverse groups. This study aims to enhance sign language proficiency for deaf-mute and hearing individuals by utilizing computer vision technology to create an interactive, inclusive sign language dictionary. This tool will facilitate independent learning, improve communication accuracy, and promote inclusive social interactions. We hypothesize that computer vision integration will make sign language learning more accessible and efficient, thereby fostering a more inclusive society and improving psychological well-being. Our approach included analyzing current sign language learning methods, exploring open databases like WLASL with educational video content, and developing a gesture recognition algorithm using computer vision. We programmed algorithms for real-time gesture recognition via webcam, enabling users to compare their gestures with standard models and receive immediate feedback. An interactive user interface supports various sign languages, allowing users to practice specific gestures. The application was tested with a target group to assess usability and gesture recognition accuracy. The dictionary application achieved an 85% accuracy rate in gesture recognition, allowing users to practice gestures in real-time and facilitating smoother self-study. Feedback from test users indicated that the tool effectively supported learning new gestures and improving proficiency. This project advances accessible tools for sign language learning, enhancing communication between deaf-mute and hearing communities. Findings confirm that computer vision effectively improves gesture recognition and communication skills. Future work will expand the gesture database and enhance feedback mechanisms for greater accuracy.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Using Computer Vision and Open Data to Improve Sign Language Proficiency in an Inclusive Communication Environment

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Hlib Stupak,
  • Hanna Telychko,
  • Daria Lytvyn,
  • Mykyta Telychko

摘要

The World Health Organization states that over 5% of the global population—approximately 430 million people—experience some degree of deafness and muteness. The lack of sign language proficiency among the general population creates communication barriers, impacting the emotional well-being of both deaf-mute and hearing individuals. Developing accessible tools for self-guided sign language learning is crucial for fostering mental health and mutual understanding across diverse groups. This study aims to enhance sign language proficiency for deaf-mute and hearing individuals by utilizing computer vision technology to create an interactive, inclusive sign language dictionary. This tool will facilitate independent learning, improve communication accuracy, and promote inclusive social interactions. We hypothesize that computer vision integration will make sign language learning more accessible and efficient, thereby fostering a more inclusive society and improving psychological well-being. Our approach included analyzing current sign language learning methods, exploring open databases like WLASL with educational video content, and developing a gesture recognition algorithm using computer vision. We programmed algorithms for real-time gesture recognition via webcam, enabling users to compare their gestures with standard models and receive immediate feedback. An interactive user interface supports various sign languages, allowing users to practice specific gestures. The application was tested with a target group to assess usability and gesture recognition accuracy. The dictionary application achieved an 85% accuracy rate in gesture recognition, allowing users to practice gestures in real-time and facilitating smoother self-study. Feedback from test users indicated that the tool effectively supported learning new gestures and improving proficiency. This project advances accessible tools for sign language learning, enhancing communication between deaf-mute and hearing communities. Findings confirm that computer vision effectively improves gesture recognition and communication skills. Future work will expand the gesture database and enhance feedback mechanisms for greater accuracy.