%0 Journal Article
%T A SCRIPT INDEPENDENT APPROACH FOR HANDWRITTEN BILINGUAL KANNADA AND TELUGU DIGITS RECOGNITION
%A DHANDRA B.V.
%A GURURAJ MUKARAMBI
%A MALLIKARJUN HANGARGE
%J International Journal of Machine Intelligence
%D 2011
%I Bioinfo Publications
%X In this paper, handwritten Kannada and Telugu digits recognition system is proposed based on zone features.The digit image is divided into 64 zones. For each zone, pixel density is computed. The KNN and SVM classifiers areemployed to classify the Kannada and Telugu handwritten digits independently and achieved average recognition accuracyof 95.50%, 96.22% and 99.83%, 99.80% respectively. For bilingual digit recognition the KNN and SVM classifiers are usedand achieved average recognition accuracy of 96.18%, 97.81% respectively
%K OCR
%K Zone Features
%K KNN
%K SVM
%U http://www.bioinfo.in/uploadfiles/13252457173_3_16_IJMI%20.pdf