Performance analysis of feature extractors and classifiers for script recognition of English and Gurmukhi words

Script Recognition is a challenging field for the recognition of documents in a multilingual country like India where different scripts are in use. For optical character recognition of such multilingual documents, it is necessary to separate blocks, lines, words and characters of different scripts before feeding them to the OCRs of individual scripts. Many approaches have been proposed by the researchers towards script recognition at different levels (Block, Line, Word and Character Level). Normally Indian documents, in any its state language contain English words mixed with other words in its own state language. In this paper, we extract three different types of features: Structural, Gabor and Discrete Cosine Transforms(DCT) Features from Isolated English and Gurmukhi words and compare their script recognition performance using three different classifiers: Support Vector Machine (SVM), k-Nearest Neighbor and Parzen Probabilistic Neural Network (PNN).