A Novel Approach to Text Dependent Writer Identification of Kannada Handwriting

In this paper a text dependent writer identification method based on Kannada handwriting is proposed. The database of Kannada handwritten words collected from 25 writers is intended to provide training and testing sets for Kannada writer identification research, because there is no well-known database containing Kannada handwritten words available for writer identification problem. A feature vector consisting of directional multi-resolution spatial features based on Radon Transform and Discrete Cosine Transform and structural features such as aspect ratio and on-pixel ratio are extracted from word images. The novel approach of combining the features of two or more words is proposed and performance analysis is done through nearest neighbor classifier with modified 5-fold cross-validation. An average identification accuracy of 93.2582% is achieved by using only the single words, and accuracy of 100% is achieved using the combination of features of three or more words. The writer identification results show that feature vectors extracted from longer words, words having more structural variation and combination of features of two or more words have higher impact on writer identification.