Automatic categorization of tax forms and component block decomposition

Abstract

This paper investigates methods for classifying types of tax forms and decomposing component blocks of tax forms automatically. We considered three methods. In the first method, the input image was first separated into component blocks; then types of tax forms were identified by comparing the component blocks with component blocks of template images. In the second and third methods, a type of input tax form was identified first and then form models and registration techniques were used to decompose the component blocks of the input tax form. In the second method, the type of tax form was identified by matching the tax form type image of an input tax form image and a prototype tax form type image, using a correlation coefficient. The last method identified the type of input tax form by recognizing characters and digits on the top of an input tax form. Experiments were performed on 520 tax form images composed of 26 types, each with 20 images. The first method achieved a 61.15% average correct classification result but it could not extract all component blocks correctly. With regard to the second and third methods, the accuracy rates of tax form identification were 88.65% and 100%, respectively, and they could extract all component blocks on tax forms correctly by using form models for component block decomposition. The results here showed that the method of using the character recognition on the tax form type and the form model had potential to be applied to develop the system for classifying type of tax form images and decomposing component blocks of tax form images.