Identification of Machine-Printed and Handwritten Words in Arabic and Latin Scripts

Abstract

Our ultimate objective is to contribute to the field of script and nature identification to be able to differentiate, at word level, handwritten or machine-printed, Arabic and Latin scripts. Different sets of features have been employed successfully for discriminating between Arabic and Latin words. They include few well-established features previously used and adapted in our case and new structural features which are intrinsic features of Arabic and Latin scripts. We select features that maximize the distinction between Arabic and Latin words. Experiments have been conducted with 1320 handwritten and printed words, covering a wide range of fonts, and encouraging results have been obtained. We achieved a correct classification of 98.4 percent for word level script and nature identification using Bayes classifier.