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In this paper, we use statistical texture features for handwritten andprinted text classification. We primarily aim for word level classification insouth Indian scripts. Words are first extracted from the scanned document. Foreach extracted word, statistical texture features are computed such as mean,standard deviation, smoothness, moment, uniformity, entropy and local rangeincluding local entropy. These feature vectors are then used to classify wordsvia k-NN classifier. We have validated the approach over several differentdatasets. Scripts like Kannada, Telugu, Malayalam and Hindi i.e., Devanagariare primarily employed where an average classification rate of 99.26% isachieved. In addition, to provide an extensibility of the approach, we addressRoman script by using publicly available dataset and interesting results arereported.

Statistical Texture Features based Handwritten and Printed Text Classification in South Indian Documents