Evaluation of Machine Learning Models for Real-Time Sign Recognition
M Sirisha, Billakurthi Sai Sanjana, Vishal Goutham N, Surekha Borra · 2021 IEEE Mysore Sub Section International Conference (MysuruCon) · 2021
Development of a real-time sign recognition system is a critical step in enabling communication between the speech impaired and normal individuals. This paper proposes a vision-based model for sign recognition using Machine Learning models. A local dataset is created by capturing the images under various light and background conditions by employing a webcam. AlexNet architecture and MATLAB are used to extract the features from captured images and to train several Machine Learning models for their performance evaluation. The images captured in real-time are processed to extract the features and then are directed to the classifier to predict the Sign class. The text to voice conversion module plays the speech based on the output of the classifier. An accuracy of 91.7 % is achieved with Support Vector Machine (SVM) learning model on the private dataset. Sign recognition with the proposed model is more reliable and cost-effective when compared to non-vision based approach.