Novel Deep Neural Network Model for Handwritten Digit Classification and Recognition

Abstract

Deep neural network is a technique of deep learning, where deep neural network model have multiple hidden layers with input and output layer, but artificial neural network have single hidden layer between input and output layer. The use of multiple hidden layers in deep neural network is to improve the performance of model and achieving the higher accuracy compare to machine learning models and their accuracy. The field of pattern recognition is mostly used by the researchers for their research work. There are lots of pattern are available in the field of pattern recognition like: handwritten digits, characters, images, faces, sound, speech etc. In this paper we have concentrated on handwritten digits classification and recognition. For handwritten digit datasets, we have used commonly known Arkiv Digital Sweden (ARDIS) [1] dataset and United State postal service (USPS) [7] dataset. ARDIS dataset is a collection of 7600 samples, where 6600 used as training samples and 1000 used as testing samples. USPS dataset is a collection of 10000 image samples where 7291 samples are used as training sample and 2007 samples are used as testing samples. In this paper we have implemented the proposed deep neural network technique for the classification and recognition of the ARDIS and USPS dataset. The proposed model has collection of 6 layers with relu and softmax activation function. After implementing model, 98.70% testing and 99.76% training accuracy for ARDIS samples achieved, which is higher than previous research accuracy. Also 98.22% training and 93.01%testing accuracy with USPS samples dataset has been achieved. The results represents the performance of deep neural networks have been outstanding compare to other previous techniques.