On-line Handwritten Gujarati Character Recognition Using Low Level Stroke

This paper presents a low level stroke feature based method for recognition of online handwritten Gujarati characters and numerals. A reasonable size database of online handwritten Gujarati characters and numerals has been developed. This is the first such database of online handwritten symbols for Gujarati script. The hierarchical histograms of twelve different low level stroke features and eight directional features were generated to capture the variation in strokes at different level. Recognition is performed using a nearest neighbor (i.e. K-NN) classifier with k-fold cross validation on the dataset having 4500 samples from 45 different classes (37 characters and 8 numerals). Overall Recognition rates achieved are 95%, 93% and 90% for numerals dataset, characters dataset and combine dataset of numerals and characters respectively.