Title :
Handwritten Character Recognition Using Gray-scale Based State-Space Parameters and Class Modular NN
Author_Institution :
Tata Consultancy Services Ltd., Mumbai
Abstract :
We present a novel feature extraction method for offline recognition of segmented Malayalam handwritten characters from their gray-scale images without the usual step of binarization. We investigate a new approach to model handwritten characters using state-space map (SSM) and state-space point distribution (SSPD) parameters. In the recognition stage we used class modular neural network with the proposed SSPD features and this method is found to be promising.
Keywords :
feature extraction; handwritten character recognition; image segmentation; natural language processing; neural nets; Malayalam handwritten character segmentation; class modular neural network; feature extraction; gray-scale image; handwritten character recognition; offline recognition; state-space map; state-space point distribution; Character recognition; Delay; Feature extraction; Gray-scale; Handwriting recognition; Humans; Image reconstruction; Natural languages; Neural networks; Signal processing;
Conference_Titel :
Signal Processing, Communications and Networking, 2008. ICSCN '08. International Conference on
Conference_Location :
Chennai
Print_ISBN :
978-1-4244-1924-1
Electronic_ISBN :
978-1-4244-1924-1
DOI :
10.1109/ICSCN.2008.4447222
