Image Analysis Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of page images using the area Voronoi diagram
Computer Vision and Image Understanding - Special issue on document image understanding and retrieval
Two Geometric Algorithms for Layout Analysis
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Structure in On-line Documents
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Mode Detection and Incremental Recognition
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Learning Non-Generative Grammatical Models for Document Analysis
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Performance Evaluation and Benchmarking of Six-Page Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IBM Journal of Research and Development
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The aim of this paper is to explore how well the task of text vs. nontext distinction can be solved in online handwritten documents using only offline information. Two systems are introduced. The first system generates a document segmentation first. For this purpose, four methods originally developed for machine printed documents are compared: x-y cut, morphological closing, Voronoi segmentation, and whitespace analysis. A state-of-the art classifier then distinguishes between text and non-text zones. The second system follows a bottom-up approach that classifies connected components. Experiments are performed on a new dataset of online handwritten documents containing different content types in arbitrary arrangements. The best system assigns 94.3% of the pixels to the correct class.