Urdu Sign Language Reorganization via Artificial Neural Networks

Abstract

Sign languages display the same linguistic characteristics as oral languages and utilize the same language services. Sign language processing solutions provide a communication link for persons with hearing impairments and healthy persons. Without these icons' ability to understand, deaf children experience several challenges in learning social norms and cannot meet adults to exchange knowledge. Parents find it challenging to express their messages to their deaf children and not hear their children. This paper focused on establishing Urdu sign language to reduce the communication barrier between ordinary folks and physically impaired people. The present study observed the Urdu Sign Language in deaf children. In this paper, the process of detecting Urdu sign language alphabets is proposed. All the 37 alphabets are identified by using KNN, ANN, and SVM classifiers. Through these alphabets, the teachers at schools and the parents at home can communicate efficiently with their deaf children. Histogram of Gradient technique is used for feature extraction. Urdu Alphabetic are identified. Maximum accuracy is obtained by using a KNN classifier that was 99, which is a significant contribution. Our proposed results are comparable to the state of the art techniques.