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Ruling-based table analysis for noisy handwritten documents

Table analysis can be a valuable step in document image analysis. In the case of noisy handwritten documents, various artifacts complicate the task of locating tables on a page and segmenting them into cells. Our ruling-based approach first detects line segments to ensure high recall of table rulings, and then computes the intersections of horizontal and vertical rulings as key points. We then employ an optimization procedure to select the most probable subset of these key points which constitute the table structure. Finally, we decompose a table into a 2-D arrangement of cells using the key points. Experimental evaluation involving 61 handwritten pages from 17 table classes show a table cell precision of 89% and a recall of 88%.