Table-form document structure analysis is an important problem in the document processing domain. This paper presents a new method called Box-Driven Reasoning (BDR) to robustly analyze the structure of table-form documents that include touching characters and broken lines. Real documents are copied repeatedly and overlaid with printed data, resulting in characters that touch cells and lines that are broken. Most previous methods employ a line-oriented approach, but touching characters and broken lines make the procedure fail at an early stage. BDR deals with regions directly in contrast with other previous methods and a reduced resolution image is introduced to supplement information deteriorated by noise. Experimental tests show that BDR reliably recognizes cells and strings in document images with touching characters and broken lines.

The issue about document structure recognition and document understanding is today one of interesting subjects from a viewpoint of practical applications. The research objective is to extract the meaningful data from document images interpretatively and also classify them as the predefined item data automatically. In comparison with the traditional image-processing-based approaches, the knowledge-based approaches, which make use of various knowledge in order to interpret structural/constructive features of documents, have been currently investigated as more flexible and applicable methods. In this paper, we propose a totally integrated paradigm for understanding table-form documents from a viewpoint of the architectural framework.