Handwritten Character Recognition Using Unique Feature Extraction Technique

One of the most arduous and captivating domains under image processing is handwritten character recognition. In this paper we have proposed a feature extraction technique which is a combination of unique features of geometric, zone-based hybrid, gradient features extraction approaches and three diff...

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Bibliographic Details
Published in:arXiv.org 2020-01
Main Authors: Ayyadevara, Sai Abhishikth, P N V Sai Ram Teja, Bharath, K P, Rajesh, Kumar M
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Back propagation
Character recognition
Comparative analysis
Feature extraction
Feature recognition
Handwriting recognition
Image processing
Multilayer perceptrons
Neural networks
Object recognition
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Summary:One of the most arduous and captivating domains under image processing is handwritten character recognition. In this paper we have proposed a feature extraction technique which is a combination of unique features of geometric, zone-based hybrid, gradient features extraction approaches and three different neural networks namely the Multilayer Perceptron network using Backpropagation algorithm (MLP BP), the Multilayer Perceptron network using Levenberg-Marquardt algorithm (MLP LM) and the Convolutional neural network (CNN) which have been implemented along with the Minimum Distance Classifier (MDC). The procedures lead to the conclusion that the proposed feature extraction algorithm is more accurate than its individual counterparts and also that Convolutional Neural Network is the most efficient neural network of the three in consideration.
ISSN:2331-8422