Sign Language Interface project aims to improve accessibility of technology for individuals who use sign languages by incorporating sign language recognition, generation, translation, and tutoring functionality into UI/UX design and AI systems.
sign language recognitiongesture interfacesign language processingUXUI
Main goals:
To create a sign language search engine that enables users to easily find sign language content.(MVP - Minimal Viable Product)
To create an interactive sign language tutoring system that effectively teaches users sign language.(Medium Viable Product)
To create a machine translation system that accurately and efficiently translates between sign languages and spoken or written language, making communication more accessible for users.(Maximal Viable Product)
Terms & Definitions
Minimal Viable Product - The minimum set of features necessary to test the basic functionality of the sign language interface.
Medium Viable Product - An expanded version of the MVP with additional features to improve usability and functionality.
Maximal Viable Product - The final version of the product, with all the features and capabilities required for full functionality.
The tasks can include
Develop sign language generation and recognition system
Develop a tool for collecting and labeling sign language data, and create a dataset using this tool
Use the tool to mark up sign language data, such as videos or images, with linguistic and grammatical information, such as signs, movements, handshape, hand orientation, location, and facial expression
Develop a sign language search engine that allows users to search for sign language content.
Develop an interactive sign language tutoring system
Continuously monitor and analyse the performance of the system to identify areas for improvement.
Design a UI/UX interface that is easy to navigate and understand for sign language users.
Recognize hand configurations-orientations to present examples of words youtube
Static one-handed gestures to present examples of words youtube
In the context of the course 'Designing a Machine Learning System', a Telegram bot is demonstrated that is able to recognize the shape of the hand and then match it to the corresponding word in Russian Sign Language. youtube (see 4:42:37-5:03:16)
Julia Prikhodko - Sign language data labeler and informer
Mark Belousov - Frontend-developer, system analyst
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