Document image processing using irregular pyramid structure

Abstract

This thesis will present our research in the use of a new irregular pyramid structure in document image processing. The focus is in the segmentation of textual components from binary, gray scale and color document images with mixed texts/graphics. The thesis presents our solution to address a common problem in handling documents with texts in varying sizes and orientations during segmentation where most methods have assumed a Manhattan or a dominant skew document layout. The solution extends beyond the isolation of word groups to the identification of logical text groups containing word groups with non-uniform orientations. It also presents an adaptive thresholding solution which does not require the pre-determination of a fixed local window size for the binarization of grey scale textual objects. Finally the thesis discusses our solution in the segmentation of textual regions from color document images where others have problems in the isolation of textual components as a compact region.