A Comparative Analysis of American Sign Language Recognition Based on CNN, KNN And Random Forest Classifier

Abstract

There is a body language based communication way for deaf and mute people called sign language. But it is very difficult to understand sign language for a normal person. For that reason, mute and deaf people are unable to express what is in their mind. There are many sign languages all over the world such as ASL, BSL etc. A part of American Sign Language(ASL) represents English alphabets by hand gesture and our dataset based on this part. This paper represents a Comparative analysis of American Sign Language Recognition. Where system uses a vast ASL dataset from the MNIST database applied three algorithms as CNN, KNN and Random Forest to create the system and analyze comparatively. This analysis is based on execution time and accuracy rate. Where CNN produces the most efficient result. It’s recognized with good generalization ability. Further research and more can produce an ASL based sign language interpreter system. Then it will be the easier way to communicate with deaf and mute people.