Knowledge-Based Partial Matching: An Efficient Form Classification Method
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
An Efficient Form Classification Method Using Partial Matching
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
A Statistical Method for an Automatic Detection of Form Types
DAS '98 Selected Papers from the Third IAPR Workshop on Document Analysis Systems: Theory and Practice
Form Analysis by Neural Classification of Cells
DAS '98 Selected Papers from the Third IAPR Workshop on Document Analysis Systems: Theory and Practice
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A new method of image understanding for forms based on model matching is proposed in this paper as the basis of OCR which can read a variety of forms. The outline of this method is described as follows. Ruled lines are extracted from the input image of a form. These lines are used for understanding the form, taking into account their feature attributes and the relationships between them. Each line in the input image of a form as expected to correspond to a line in one of the model forms, which are described as structured features. This correspondence is represented by a node in an association graph where an arc represents compatible correspondences established on the basis of feature relationships. The best match is found as the largest maximal clique in the association graph. Experimental results show the method is robust and effective for poor quality document images and also for various styles of forms.