Open AccessHandwritten Gurmukhi Digit Prediction using Convolutional Neural Network with Keras and Tensorflow
Author(s) -
Sonia Flora,
Divya Ebenezer Nathaniel
Publication year - 2019
Publication title -
international journal of scientific research in science, engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-1990
pISSN - 2394-4099
DOI - 10.32628/ijsrset1962169
Subject(s) - convolutional neural network , computer science , artificial intelligence , neocognitron , character (mathematics) , handwriting recognition , artificial neural network , pattern recognition (psychology) , handwriting , deep learning , intelligent word recognition , speech recognition , intelligent character recognition , character recognition , optical character recognition , feature extraction , image (mathematics) , time delay neural network , mathematics , geometry
Intelligent Character Recognition is a term which is specifically used for the recognition of handwritten character or digit. It is a prominent research area of computer vision field of machine learning or deep learning which trained the machine to analyze the pattern of handwritten character image and identify it. Recognition of handwritten character is a hard process because single person can handwrite the same text in number of ways by making a little variation in holding the pen. Handwriting has no specific font style or size. It differs person to person or more specifically it differs how one is holding the pen. Deep Leaning has brought the breakthrough performance in this research area with its dedicated models like Convolutional Neural Network, Recurrent Neural Network etc. In this paper, we have trained model with Convolutional Neural Network with different number of layers and filters over 10,559 handwritten gurmukhi digit images and validate over 1320 images. Consequently we could achieve the maximum accuracy of 99.24%.