Hierarchical content classification and script determination for automatic document image processing

Abstract

Page segmentation and image content classification plays an important role in automatic document image processing with applications to mixed-type document image compression, form and check reading, and automatic mail sorting. In this paper, we propose an enhanced background-thinning based page segmentation algorithm to process document images rapidly and eliminate some small regions embedded in other regions. We then present a hierarchical approach, which combines cross correlation measure, Kolmogorov complexity measure, and a neural network, to classify sub-images into halftones and texts. The approach also achieves high accuracy in text determination using a three-layer feed-forward network, where text region can be classified into Chinese or alphabetic character. Experimental results on a number of mixed-type document images show the efficiency and effectiveness of our approach. © 2002 IEEE.