1
Department of Electronics and Comunication Engg, Pondicherry Engineering College
2
EEE, PEC
Abstract
Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications that includes, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. Neural Network (NN) with its inherent learning ability offers promising solutions for handwritten character recognition. This paper identifies the most suitable NN for the design of hand written English character recognition system. Different Neural Network (NN) topologies namely, back propagation neural network, nearest neighbour network and radial basis function network are built to classify the characters. All the NN based Recognition systems use the same training data set and are trained for the same target mean square error. Two hundred different character data sets for each of the 26 English characters are used to train the networks. The performance of the recognition systems is compared extensively using test data to draw the major conclusions of this paper
PRADEEP, J., Srinivasan, E., & Himavathi, S. (2012). Neural Network Based Recognition System Integrating Feature Extraction and Classification for English Handwritten. International Journal of Engineering, 25(2), 99-106.
MLA
J. PRADEEP; E Srinivasan; S Himavathi. "Neural Network Based Recognition System Integrating Feature Extraction and Classification for English Handwritten". International Journal of Engineering, 25, 2, 2012, 99-106.
HARVARD
PRADEEP, J., Srinivasan, E., Himavathi, S. (2012). 'Neural Network Based Recognition System Integrating Feature Extraction and Classification for English Handwritten', International Journal of Engineering, 25(2), pp. 99-106.
VANCOUVER
PRADEEP, J., Srinivasan, E., Himavathi, S. Neural Network Based Recognition System Integrating Feature Extraction and Classification for English Handwritten. International Journal of Engineering, 2012; 25(2): 99-106.
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