%0 Journal Article %1 IJACSA.2011.020521 %A Prerna Singh, Nidhi Tyagi %D 2011 %J International Journal of Advanced Computer Science and Applications(IJACSA) %K (PCA) Analysis Basis Component Devanagari Function; K-means Numeral Principal Radial Recog- clustering; nition; %N 5 %T Radial Basis Function For Handwritten Devanagari Numeral Recognition %U http://ijacsa.thesai.org/ %V 2 %X The task of recognizing handwritten numerals, using a classifier, has great importance. This paper applies the technique of Radial Basis Function for handwritten numeral recognition of Devanagari Script. Lot of work has been done on Devanagari numeral recognition using different techniques for increasing the accuracy of recognition. Since the database is not globally created, firstly we created the database by implementing pre-processing on the set of training data. Then by the use of Principal Component Analysis we have extracted the features of each image, some researchers have also used density feature extraction. Since different people have different writing style, so here we are trying to form a system where recognition of numeral becomes easy. Then at the hidden layer centers are determined and the weights between the hidden layer and the output layer of each neuron are determined to calculate the output, where output is the summing value of each neuron. In this paper we have proposed an algorithm for determining Devanagari numeral recognition using the above mentioned system

@article{IJACSA.2011.020521, abstract = {The task of recognizing handwritten numerals, using a classifier, has great importance. This paper applies the technique of Radial Basis Function for handwritten numeral recognition of Devanagari Script. Lot of work has been done on Devanagari numeral recognition using different techniques for increasing the accuracy of recognition. Since the database is not globally created, firstly we created the database by implementing pre-processing on the set of training data. Then by the use of Principal Component Analysis we have extracted the features of each image, some researchers have also used density feature extraction. Since different people have different writing style, so here we are trying to form a system where recognition of numeral becomes easy. Then at the hidden layer centers are determined and the weights between the hidden layer and the output layer of each neuron are determined to calculate the output, where output is the summing value of each neuron. In this paper we have proposed an algorithm for determining Devanagari numeral recognition using the above mentioned system}, added-at = {2014-02-21T08:00:08.000+0100}, author = {{Prerna Singh}, Nidhi Tyagi}, biburl = {https://www.bibsonomy.org/bibtex/2fa7f533afde624ed5015bb7d249656ff/thesaiorg}, interhash = {e2eaadfec438316b61d632ed9b20059d}, intrahash = {fa7f533afde624ed5015bb7d249656ff}, journal = {International Journal of Advanced Computer Science and Applications(IJACSA)}, keywords = {(PCA) Analysis Basis Component Devanagari Function; K-means Numeral Principal Radial Recog- clustering; nition;}, number = 5, timestamp = {2014-02-21T08:00:08.000+0100}, title = {{Radial Basis Function For Handwritten Devanagari Numeral Recognition}}, url = {http://ijacsa.thesai.org/}, volume = 2, year = 2011 }