Context-aware and content-based dynamic Voronoi page segmentation
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Automatic localization of page segmentation errors
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
Using a boosted tree classifier for text segmentation in hand-annotated documents
Pattern Recognition Letters
Automatic localization and correction of line segmentation errors
Proceeding of the workshop on Document Analysis and Recognition
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This paper presents a dynamic approach to document page segmentation. Current page segmentation algorithms lack the ability to dynamically adapt local variations in the size, orientation and distance of components within a page. Our approach builds upon one of the best algorithms, Kise et. al. work based on Area Voronoi Diagrams, which adapts globally to page content to determine algorithm parameters. In our approach, local thresholds are determined dynamically based on parabolic relations between components, and Docstrum based angular and neighborhood features are integrated to improve accuracy. Zone-based evaluation was performed on four sets of printed and handwritten documents in English and Arabic scripts and an increase of 33% in accuracy is reported.