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Complex documents images segmentation based on steerable pyramid features.

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Bibliographic Details
Title: Complex documents images segmentation based on steerable pyramid features.
Authors: Benjelil, Mohamed1,2 benjlaiel@yahoo.fr, Kanoun, Slim1 slim.kanoun@yahoo.fr, Mullot, Rémy2 remy.mullot@univ-lr.fr, Alimi, Adel M.1 adel.alimi@ieee.org
Source: International Journal on Document Analysis & Recognition. Sep2010, Vol. 13 Issue 3, p209-228. 20p. 10 Diagrams, 7 Charts, 3 Graphs.
Subjects: Document selection, Visual communication, Graphology, Logos (Symbols), Document imaging systems
Abstract: Page segmentation and classification is very important in document layout analysis system before it is presented to an OCR system or for any other subsequent processing steps. In this paper, we propose an accurate and suitably designed system for complex documents segmentation. This system is based on steerable pyramid transform. The features extracted from pyramid sub-bands serve to locate and classify regions into text (either machine-printed or handwritten) and non-text (images, graphics, drawings or paintings) in some noise-infected, deformed, multilingual, multi-script document images. These documents contain tabular structures, logos, stamps, handwritten script blocks, photographs, etc. The encouraging and promising results obtained on 1,000 official complex document images data set are presented in this research paper. We compared our results with those from existing state-of-the-art methods. This comparison shows that the proposed method performs consistently well on large sets of complex document images. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Page segmentation and classification is very important in document layout analysis system before it is presented to an OCR system or for any other subsequent processing steps. In this paper, we propose an accurate and suitably designed system for complex documents segmentation. This system is based on steerable pyramid transform. The features extracted from pyramid sub-bands serve to locate and classify regions into text (either machine-printed or handwritten) and non-text (images, graphics, drawings or paintings) in some noise-infected, deformed, multilingual, multi-script document images. These documents contain tabular structures, logos, stamps, handwritten script blocks, photographs, etc. The encouraging and promising results obtained on 1,000 official complex document images data set are presented in this research paper. We compared our results with those from existing state-of-the-art methods. This comparison shows that the proposed method performs consistently well on large sets of complex document images. [ABSTRACT FROM AUTHOR]
ISSN:14332833
DOI:10.1007/s10032-010-0113-9