Classification of Handwritten Document Image into Text and Non-Text Regions

Segmentation of document image into text and non-text regions is an essential process in document layout analysis which is one of the preprocessing steps in optical character recognition. Usually handwritten documents has no specific layout. It may contain non text regions such as diagrams, graphics, tables etc. In this work we propose a novel approach to segment text and non text components in Malayalam handwritten document image using Simplified Fuzzy ARTMAP (SFAM) classifier. Binarized document image is dilated horizontally and vertically and merged together. Perform connected component labelling on the smeared image. A set of geometrical and statistical features are extracted from each component and given to SFAM for classifying it into text and non text components. Experimental results are promising and it can be extended to other scripts also.

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