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.
