Page segmentation is necessary for optical character recognition and very useful in document image manipulation. This paper describes two classification methods, a modified linear adaptive method and a proposed neural network system that classifies an image into text, halftone image (photos, dark images, etc.), and graphics (graphs, tables, flowcharts, etc.). The blocks were segmented using the Run Length Smearing Algorithm. The smearing process was done automatically by fixing the threshold values for smearing. Features are extracted from the segmented blocks for classification into text, graphics, and halftone images. The second method uses a multi-layer perceptron neural network for classification. Two parameters, a shape factor, f1, and an angle from the rectangular block segments, were fed into the neural network system giving us three classes: text, halftone images, and graphics. Experiments on 30 mixed-content document images show that the method works well on a wide variety of layouts in document images.
Development of a Genetic based Neural Network System for Online Character Recognition
