Handwritten Hindi Digits Recognition Using Convolutional Neural Network with RMSprop Optimization
R. Vijaya Kumar Reddy, B. Srinivasa Rao, K. Prudvi Raju · 2018
An efficient handwritten Hindi numeral digit recognition structure based on Convolutional Neural Network (CNN) with RMSprop optimization technique is present in this paper. Convolutional Neural Networks as a powerful feature extraction do not use the predefined kernels, but instead they learn data from specific kernels. The structural design of the network consists of convolutional (Conv2D) layer, pooling (MaxPool2D) layer, Flatten layer and two fully-connected layers. Where a sliding window function is applied to a matrix of a numerical image. We evaluated our scheme on 20,000 handwritten samples of Hindi numerals from kaggle dataset and from this experiment we achieved 99.85% recognition rate by the proposed method Convolutional Neural Network with RMSprop (Root Mean Square Propagation) Optimization which is very promising results.