Segmentation of page images using the area Voronoi diagram
Computer Vision and Image Understanding - Special issue on document image understanding and retrieval
Empirical Performance Evaluation Methodology and Its Application to Page Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Document Spectrum for Page Layout Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
On Segmentation of Documents in Complex Scripts
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Content-level Annotation of Large Collection of Printed Document Images
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Google Book Search: Document Understanding on a Massive Scale
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
Performance Evaluation and Benchmarking of Six-Page Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Voronoi++: A Dynamic Page Segmentation Approach Based on Voronoi and Docstrum Features
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
ICDAR 2009 Page Segmentation Competition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
IBM Journal of Research and Development
Learning to segment document images
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Automatic localization and correction of line segmentation errors
Proceeding of the workshop on Document Analysis and Recognition
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Page segmentation is a basic step in any character recognition system. Its failure is one of the major causes for deteriorating overall accuracy of the current Indian language OCR engines. Many segmentation algorithms are proposed in literature. Often these algorithms fail to adapt dynamically to a given page and thus tend to yield poor segmentation for some specific regions or some specific pages. Given the ground truth, locating page segmentation errors is a straight foreword problem and merely useful for comparing segmentation algorithms. In this work, we locate segmentation errors without directly using the ground truth. Such automatic localization of page segmentation errors can be considered a major step towards improving page segmentation errors. In this work, we focus on localizing line level segmentation errors. We perform experiments on more than 18000 scanned pages of 109 books belonging to four prominent south Indian languages.