DocumentCode :
604596
Title :
An efficient handwritten Devnagari character recognition system using neural network
Author :
Sahu, Nilkanta ; Raman, N.K.
Author_Institution :
Comput. Sci. Dept., ITM Univ., Gurgaon, India
fYear :
2013
fDate :
22-23 March 2013
Firstpage :
173
Lastpage :
177
Abstract :
Character recognition systems for various languages and script has gain importance in recent decades and is the area of deep interest for many researchers. Their development is strongly integerated with Neural Networks. But, recognizing Devanagari Script is relatively greater challenge due to script´s complexity. Various techniques have been implemented for this problem with many improvements so far. This paper describes the development and implementation of one such system comprising combination of several stages. Mainly Artificial Neural Network technique is used to designed to preprocess, segment and recognize devanagari characters. The system was designed, implemented, trained and found to exhibit an accuracy of 75.6% on noisy characters.
Keywords :
backpropagation; feature extraction; graphical user interfaces; handwritten character recognition; image segmentation; natural language processing; neural nets; optical character recognition; Devanagari character preprocess; Devanagari character segmentation; Devanagari script recognition; GUI; OCR; artificial neural network technique; back propagation neural network; feature extraction; handwritten Devnagari character recognition system; script complexity; Accuracy; Biological neural networks; Character recognition; Feature extraction; Image segmentation; Training; Devnagari Character Recognition; Feature Extraction; OCR; Off-line Handwriting Recognition; Segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), 2013 International Multi-Conference on
Conference_Location :
Kottayam
Print_ISBN :
978-1-4673-5089-1
Type :
conf
DOI :
10.1109/iMac4s.2013.6526403
Filename :
6526403
Link To Document :
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