Automated Sign Language Recognition with Machine Learning

The purpose of this research is to design a system using machine learning to automate sign language gesture recognition and facilitate unproblematic interaction between the hearing/speech impaired community and society as a whole. This research works towards solving the communication disability faced by the deaf and mute community through a computer vision framework for recognizing hand gestures and mapping them to relevant text or speech. The work employs a rich dataset of static American Sign Language (ASL) hand signs and uses Convolutional Neural Networks (CNNs) for efficient feature extraction and gesture classification. The suggested model is trained and evaluated on a huge collection of labeled gesture images and exhibits high accuracy with robust generalization to novel data. One of the primary contributions of the work is the implementation of the trained model into an interactive, realtime graphical user interface developed with Python's Tkinter library. The interface captures live input from a webcam, processes each frame to identify gestures, and shows the identified character on the screen. The results of the evaluation confirm the ability of CNNs to accurately identify ASL alphabets with little delay. The proposed system presents an effective and scalable approach towards real-time sign language interpretation, supporting inclusiveness and accessibility of the disabled community. The present research also creates a scope for extension in development, i.e., inclusion of dynamic gesture detection and portability to mobile or embedded platforms to provide enhanced use in real-life situations.