DocumentCode :
2042112
Title :
Diagonal based feature extraction for handwritten character recognition system using neural network
Author :
Pradeep, J. ; Srinivasan, E. ; Himavathi, S.
Author_Institution :
Dept. of ECE, Pondicherry Eng. Coll., Pondicherry, India
Volume :
4
fYear :
2011
fDate :
8-10 April 2011
Firstpage :
364
Lastpage :
368
Abstract :
An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network is described in the paper. A new method, called, diagonal based feature extraction is introduced for extracting the features of the handwritten alphabets. Fifty data sets, each containing 26 alphabets written by various people, are used for training the neural network and twenty different handwritten alphabets characters are used for testing. The proposed recognition system performs quite well yielding higher levels of recognition accuracy compared to the systems employing the conventional horizontal and vertical methods of feature extraction. This system will be suitable for converting handwritten documents into structural text form and recognizing handwritten names.
Keywords :
feature extraction; feedforward neural nets; handwritten character recognition; text analysis; diagonal based feature extraction; multilayer feedforward neural network; off-line handwritten alphabetical character recognition system; structural text; Accuracy; Artificial neural networks; Character recognition; Feature extraction; Handwriting recognition; Pixel; Training; Feature extraction; Feed forward propagation Neural Network; Handwritten Character Recognition; Image; processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronics Computer Technology (ICECT), 2011 3rd International Conference on
Conference_Location :
Kanyakumari
Print_ISBN :
978-1-4244-8678-6
Electronic_ISBN :
978-1-4244-8679-3
Type :
conf
DOI :
10.1109/ICECTECH.2011.5941921
Filename :
5941921
Link To Document :
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