Open AccessClassification and Recognition of Printed Hindi Characters Using Artificial Neural Networks
Author(s) -
B. Indira,
M Shalini,
M. V. Ramana Murthy,
Mahaboob Sharief Shaik
Publication year - 2012
Publication title -
international journal of image graphics and signal processing
Language(s) - English
Resource type - Journals
eISSN - 2074-9082
pISSN - 2074-9074
DOI - 10.5815/ijigsp.2012.06.03
Subject(s) - character (mathematics) , hindi , artificial neural network , artificial intelligence , character recognition , computer science , pattern recognition (psychology) , set (abstract data type) , neocognitron , speech recognition , time delay neural network , optical character recognition , intelligent character recognition , test set , natural language processing , mathematics , image (mathematics) , geometry , programming language
Character Recognition is one of the important tasks in Pattern Recognition. The complexity of the character recognition problem depends on the character set to be recognized. Neural Network is one of the most widely used and popular techniques for character recognition problem. This paper discusses the classification and recognition of printed Hindi Vowels and Consonants using Artificial Neural Networks. The vowels and consonants in Hindi characters can be divided in to sub groups based on certain significant characteristics. For each group, a separate network is designed and trained to recognize the characters which belong to that group. When a test character is given, appropriate neural network is invoked to recognize the character in that group, based on the features in that character. The accuracy of the network is analyzed by giving various test patterns to the system.
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