What is it about?
This paper introduces Talk2Me, an innovative mobile app designed to bridge the communication gap between deaf and mute communities and the wider world. By using advanced deep learning and computer vision techniques, Talk2Me can translate Algerian sign language into written and spoken words in real-time. The app was built using a custom dataset of Algerian sign language alphabets and numbers, leveraging transfer learning with the YOLOV5S model. The result is a tool that allows individuals who use sign language to communicate effortlessly with those who don’t, all without the need for the other person to learn sign language.
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Why is it important?
Communication is a vital part of human interaction, and everyone should have the opportunity to express themselves and be understood. For those who are deaf or mute, sign language has long served as a crucial means of communication. However, sign language is not universally known, creating barriers between the deaf and mute communities and others. This lack of understanding can limit their social interactions, career opportunities, and even basic day-to-day conversations. Talk2Me aims to change that by providing an accessible tool that allows these communities to communicate freely with anyone—without the need to learn sign language.
Perspectives

The development of Talk2Me marks a significant step toward more inclusive communication. With further development, it has the potential to support not just Algerian sign language but also other regional and global sign languages. This app could be used by schools, workplaces, hospitals, and public spaces, fostering greater inclusivity and understanding in society. Moreover, the potential integration of this technology into other platforms and devices (such as smart glasses or wearable tech) could revolutionize how we bridge communication gaps across different languages and communities. This paper is not only a technological achievement but also a reminder of the power of innovation to create a more connected and inclusive world.
Leila BENAROUS
University of Laghouat
Read the Original
This page is a summary of: Using Deep Learning to Translate Algerian Sign Language to Spoken and Written Words, January 2025, Springer Science + Business Media,
DOI: 10.1007/978-3-031-82150-9_7.
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