Offline Handwritten Gurumukhi Numeral Recognition Using SVM and Different Feature Sets

Main Article Content

Ashutosh Aggarwal


solated handwritten numerals recognition has been the subject of intensive research during last decades because it is useful in wide range of real world problems. It also provides a solution for processing large volumes of data automatically.Work has beendone in recognizing handwritten characters and numerals in manylanguages like Chinese, Arabic, Devanagari, Urdu and English.Recently there is an emerging trend in the research to recognize handwritten characters and numerals of many Indian languages and scripts. In this manuscript we have practiced the recognition of handwritten Gurumukhi numerals. We have used three different feature sets. Out of three, two feature sets are based on the output of Gabor filters one being GABM having dimensionality 210 and other being GABN with dimensionality 200.Third feature set is comprised of Gradient Features forming 200 features. The SVM classifier with RBF (Radial Basis Function) kernel is used for classification. We have obtained the 5-fold cross validation accuracy as 99.7% using third feature set consisting of 200 gradient features. On second and first feature sets recognition rates 99.53% and 99% are observed. To obtain better results pre-processing of noise removal and normalization processes before feature extraction are recommended.

General Terms-Pattern Recognition, OCR, Indian Scripts, Gurmukhi script.




Download data is not yet available.

Article Details