A new benchmark on american sign language recognition using convolutional neural network

The listening or hearing impaired (deaf/dumb) people use a set of signs, called sign language instead of speech for communication among them. However, it is very challenging for non-sign language speakers to communicate with this community using signs. It is very necessary to develop an application to recognize gestures or actions of sign languages to make easy communication between the normal and the deaf community. The American Sign Language (ASL) is one of the mostly used sign languages in the World, and considering its importance, there are already existing methods for recognition of ASL with limited accuracy. The objective of this study is to propose a novel model to enhance the accuracy of the existing methods for ASL recognition. The study has been performed on the alphabet and numerals of four publicly available ASL datasets. After preprocessing, the images of the alphabet and numerals were fed to a newly proposed convolutional neural network (CNN) model, and the performance of this model was evaluated to recognize the numerals and alphabet of these datasets. The proposed CNN model significantly (9%) improves the recognition accuracy of ASL reported by some existing prominent methods