American Sign Language Alphabets Recognition using Convolutional Neural Network

Abstract

Sign language is a kind of language which is primary used for the deaf and hard of hearing and also used for those who are unable to physically speak. Sign language is only understood by a small percentage of the population. There is a huge communication gap between the deaf community and the hearing majority but it is not acceptable for a nation. Computerized sign language recognition capabilities attempt to break through this communication gap. Due to the advancement of technology, Artificial Intelligence has made human life easier by using various machine learning techniques. Computer vision is one of the most vital fields of artificial intelligence. However, utilizing computer vision to recognize American Sign Language is extremely difficult since sign language is extremely complicated and has a large inter-class variation. In the last few years, Convolutional Neural Network has become an effective way for the classification of multiclass images. In this paper, we have used Convolutional Neural Network (CNN) for the recognition of ASL alphabets. In this study, we have used the Sign Language MNIST dataset which consists of 34627 images where 27,455 training samples and 7172 testing data. The dataset includes 24 alphabets except for the letter J and Z among 26 alphabets. For the recognition of ASL alphabets, at first, we pre-processed our dataset by the normalization technique. After that, we designed a convolutional neural network architecture for extracting features from hand gesture images and then we trained our CNN model through the training dataset. We have finally evaluated our proposed CNN model based on the test dataset and obtained its accuracy to see that how many ASL alphabets recognize correctly. The proposed CNN architecture was able to achieve an accuracy of 99.78% on unseen data.

Keywords: American sign language, ASL alphabet, Convolution Neural Network (CNN), Hand gesture, Recognition.

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