Handwritten Hindi Character Recognition using Deep Learning Techniques
DOI:
https://doi.org/10.26438/ijcse/v7i2.17Keywords:
DFFNN, CNN, Softmax classifier, RMSprop and Adam Estimation, Deep LearningAbstract
In this paper we present a handwritten Hindi character recognition system based on different Deep learning technique. Handwritten character recognition plays an important role and is currently getting the attention of researchers because of possible applications in assisting technology for blind and visually impaired users, human–robot interaction, automatic data entry for business documents, etc. In this work, we propose a technique to recognize handwritten Hindi characters using deep learning approaches like Convolutional Neural Network (CNN) With Optimizer RMSprop (Root Mean Square Propagation) , Adaptive Moment (Adam) Estimation and Deep Feed Forward Neural Networks(DFFNN). The proposed system has been trained on samples of large set of database images and tested on samples images from user defines data set and from this experiment we achieved very high recognition results. Experimental results are compared with other neural network based algorithm.
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