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International Journal on Document Analysis and Recognition (IJDAR)
Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) 4/2016

01.12.2016 | Original Paper

Consensus-based clustering for document image segmentation

verfasst von: Soumyadeep Dey, Jayanta Mukherjee, Shamik Sural

Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) | Ausgabe 4/2016

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Abstract

Segmentation of a document image plays an important role in automatic document processing. In this paper, we propose a consensus-based clustering approach for document image segmentation. In this method, the foreground regions of a document image are grouped into a set of primitive blocks, and a set of features is extracted from them. Similarities among the blocks are computed on each feature using a hypothesis test-based similarity measure. Based on the consensus of these similarities, clustering is performed on the primitive blocks. This clustering approach is used iteratively with a classifier to label each primitive block. Experimental results show the effectiveness of the proposed method. It is further shown in the experimental results that the dependency of classification performance on the training data is significantly reduced.
Vorheriger Artikel A fast hierarchical method for multi-script and arbitrary oriented scene text extraction
Nächster Artikel Text/shape classifier for mobile applications with handwriting input

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Metadaten
Titel
Consensus-based clustering for document image segmentation
verfasst von
Soumyadeep Dey
Jayanta Mukherjee
Shamik Sural
Publikationsdatum
01.12.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal on Document Analysis and Recognition (IJDAR) / Ausgabe 4/2016
Print ISSN: 1433-2833
Elektronische ISSN: 1433-2825
DOI
https://doi.org/10.1007/s10032-016-0275-1

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