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A Divide-and-Merge Approach for Deep Segmentation of Document Tables

Document tables are a rich source of implicit semantics hidden in documents which could be exploited for better searching and ranking. However, a central problem in document table processing systems is the segmentation of impure and incomplete table segments. Many existing methods are limited to basic table segments of limited table layout complexity. These methods are passing through the stage of very low level segmentation thus missing many physical and logical structures of tables producing erroneous results. However, these structures have significant impact on the overall table interpretation. In order to gain the full strength of table information reusability, a deep segmentation with a layout independent representation of tables is needed. This paper propose an algorithm which performs divide and merge tasks in different phases of segmentation using matrix as intermediate model. The algorithm covers maximum layout complexity and extracts table schema, data and the set of reading paths which are then represented in a layout independent notation. We evaluated our results using standard measures and open data sets.