Sign Language Recognition using CNN | Machine Learning Projects
Sign Language Recognition and Gesture Generation System designed to bridge communication gaps between hearing-impaired and non-impaired individuals. The system incorporates multiple modules: gesture recognition using YOLOv8, sign character recognition via MediaPipe landmarks with CNN+LSTM architecture, and a sign generator that converts text input into sign language visuals. Natural Language Processing techniques enhance prediction accuracy by providing contextual word suggestions. The system achieves over 90% accuracy in both recognition tasks and offers a user-friendly interface developed with Tkinter. This work contributes to assistive technology by providing a bidirectional translation system between sign language and text, supporting both input and output modalities.
Sign language serves as a primary communication medium for millions of hearing-impaired individuals worldwide. Despite its importance, technological solutions for bridging the communication gap between hearing-impaired and non-impaired communities remain limited. This thesis addresses this gap by developing an integrated system for both sign language recognition and generation.
- The gesture recognition module focuses on 10 common gestures: ‘Call’, ‘Good’, ‘Hello’, ‘House’, ‘I Love You’, ‘No’, ‘Ok’, ‘Prayer’, ‘Thank You’, ‘Yes’
- Sign character recognition covers the entire alphabet (A-Z)
- The system operates in controlled lighting conditions for optimal performance
- The current implementation supports American Sign Language (ASL) with potential for expansion
Sign Language Detection using Machine Learning, this project developed using python programming language. This machine learning model is trained on large dataset of images with different signs so it can easily identify gesture sign, alphabet recognition and convert text to gesture.
Hand Gesture Recognition using Machine Learning
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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