Real-Time Hand Gestures recognition using Skin Masking and CNN
Ashutosh Kumar, Anupam Agrawal · 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) · 2022
This paper aims at analyzing and recognizing American Sign Language (ASL) that can be converted to text in order to facilitate communication with differently-abled people. This has been a key challenge for communication with/between differently-abled people. Many different approaches have been formulated trying to solve this problem including Principal Component Analysis (PCA), Finger Peak and Angle Calculation, and Support Vector Machine (SVM) to name a few. Here we have proposed a method for recognizing hand gestures and signs using Background Subtraction, Skin Masking, and Convolutional Neural Network (CNN) for segmentation and classification of gestures into text. The method provides a training accuracy of approx 98% in recognition under ideal conditions (simple background and good light intensity).