Robust detection of lines using the progressive probabilistic Hough transform
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
A method for table structure analysis using DP matching
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Tabular abstraction, editing, and formatting
Tabular abstraction, editing, and formatting
A survey of table recognition: Models, observations, transformations, and inferences
International Journal on Document Analysis and Recognition
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Clutter Noise Removal in Binary Document Images
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Table detection in heterogeneous documents
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
A Model-Based Ruling Line Detection Algorithm for Noisy Handwritten Documents
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Automatic table detection in document images
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Towards Semi-supervised Transcription of Handwritten Historical Weather Reports
DAS '12 Proceedings of the 2012 10th IAPR International Workshop on Document Analysis Systems
Model based table cell detection and content extraction from degraded document images
Proceeding of the workshop on Document Analysis and Recognition
Model-Based Tabular Structure Detection and Recognition in Noisy Handwritten Documents
ICFHR '12 Proceedings of the 2012 International Conference on Frontiers in Handwriting Recognition
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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%.