Segmentation and classification for mixed text/image documents using neural network

Abstract

A segmentation and classification method for separating a document image into printed character, handwritten character, photograph, and painted image regions is presented. A document image is segmented into rectangular areas. Each of which contains a cluster of image elements. A layered feed-forward neural network is then used to classify each segmented area using the histograms of gradient vector directions and luminance levels. A high classification performance was obtained, even with a small number of training samples. It is confirmed that the histograms of gradient vector directions and luminance levels are significantly effective features for the classification of the four kinds of image regions. Increasing the number of the discrimination areas improves the classification performance sufficiently even using a small number of training samples for the neural network. >