Discovering Knowledge through Multi-modal Association Rule Mining for Document Image Analysis

The paper introduces a descriptive data mining method to discover knowledge for the task of automatic categorization in document image analysis. We argue that a document image is a multi-modal unit of analysis whose semantics is deduced from a combination of textual content, layout structure and logical structure. So, the method considers simultaneously different modalities of document representation, and, therefore different types of information: spatial information derived from a complex document image analysis process (layout analysis), information extracted from the logical structure of the document (by means of document image classification and understanding) and the textual information extracted by means of an OCR. The proposed method is based on a relational data mining approach to discover association rules, where the relational setting is justified, given its appropriateness to analyze data available in more than one modality. Experimental results on a real world dataset are reported.