Digit Recognition of Hand Gesture Images in Sign Language Using Convolution Neural Network Classification Algorithm

Sign language is a language used by differently abled persons like deaf and muted people. Hearing impaired people use this language to communicate with normal people. If normal people don’t understand sign language, it is difficult to fill the bridge gap. In this manuscript, a novel approach is developed to recognize the digits of hand gesture images in sign language in order to fill the gap between normal people and deaf, dumb people. In this article, the digit dataset from digit 0 to 9 is considered and taken from Kaggle datasets. The database consists of hand gesture images from 0 to 9 digits, and each digit is having 500 sample images. Convolution neural network algorithm is applied to train the given hand gesture images of database. The evaluation matrix which is considered in the analysis is Recall, F1-score, Precision and Accuracy. More than 90% of accuracy is acquired in the experiment.