Window-Based Feature Extraction Framework for Machine-Printed/Handwritten and Arabic/Latin Text Discrimination

In this paper, we propose a new writing type and script text classification technique to recognize the identity of texts extracted from heterogeneous document images. English, French and Arabic languages are used in these documents with mixed handwritten and machine-printed types. In order to identify each text-line/word image, we propose to use 23 features computed on a fixed-length sliding window. Gaussian Mixture Models (GMMs) are used to achieve the classification objective. This framework has been tested on machine-printed and handwritten text-blocks, text-lines and words extracted from different document images of the Maurdor database. Experimental results reveal the effectiveness of our proposed system in writing type and script identification.