Faster convergence and reduction of overfitting in numerical hand sign recognition using DCNN

Hand signs and signals are the staple form of expression for the hearing and speech impaired people. Human Computer Interaction technology enable people to interact with computer machine using hand gestures. Common sign languages use separate hand signals to communicate different decimals. Recent developments in Deep Convolutional Neural Networks (DCNN) have opened the door to recognize and classify this visual form of gestures more accurately. In this paper, a layer-wise optimized neural network architecture is proposed where batch normalization contributes to faster convergence of training, and introduction of dropout technique mitigates data overfitting. Batch normalization forces each training batch toward zero mean and unit variance, leading to improved flow of gradients through the model and convergence in shorter time. Dropout forces neurons of neural network to regularize, resulting in reduced overfitting. A constructed numerical hand gesture data set is used for validating the claims based on American Sign Language system. The proposed model is shown to surpass other methods in classifying these numerical hand signs successfully.