Complex documents images segmentation based on steerable pyramid features

Benjelil, Mohamed; Kanoun, Slim; Mullot, Rémy; Alimi, Adel M.

doi:10.1007/s10032-010-0113-9

Cited by 13 publications

(8 citation statements)

References 47 publications

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“…Further developments for the application by the authors in [7][8][9][10][11][12][13] using affixal approach (AABATAS) which is guided by the structural properties of Arabic language. A well-known commercial software also was used to perform this test; Readiris Pro 10 [56] which was used by AbdelRaouf et al in [16].…”

Section: Testing the Two Arabic Ocr Applicationsmentioning

confidence: 99%

“…The system was developed by performing an Arabic text analysis method using the affixal approach [7]. The system developed through [8][9][10][11][12] using different approaches to enhance its recognition rate by adding morphological enhancement. An identification method for ultra-low-resolution Arabic word images using a stochastic approach was presented in [13].…”

Section: Related Workmentioning

confidence: 99%

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Arabic character recognition using a Haar cascade classifier approach (HCC)

AbdelRaouf

Higgins

Pridmore

et al. 2015

Pattern Anal Applic

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Optical character recognition (OCR) shows great potential for rapid data entry, but has limited success when applied to the Arabic language. Traditional OCR problems are compounded by the nature of Arabic language and because the script is heavily connected. A machine learning, Haar cascade classifier (HCC) approach was introduced by Viola and Jones (Rapid object detection using a boosted cascade of simple features. Kauai, Hawaii, 2001) to achieve rapid object detection based on a boosted cascade of simple Haar-like features. Here, that approach is applied for the first time to suit Arabic glyph recognition. HCC approach eliminates problematic steps in the preprocessing and recognition phases and, most importantly, character segmentation stage. A classifier was produced for each of the 61 Arabic glyphs that exist after the removal of diacritical marks (dots). These classifiers were trained and tested on some 2,000 images each. The system was tested with real text images and produces a recognition rate for Arabic glyphs of 87 %. The technique gives good results relative to those achieved using a commercial Arabic OCR application and existing state-of-the-art research application.

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Section: Testing the Two Arabic Ocr Applicationsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Arabic character recognition using a Haar cascade classifier approach (HCC)

AbdelRaouf

Higgins

Pridmore

et al. 2015

Pattern Anal Applic

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“…Three algorithms use only texture features [9,88,89]. Baechler and Ingold [89] string together three Dynamic Multi-Layer Perceptrons (DMLP) at three resolutions to segment historical documents.…”

Section: Texture Classificationmentioning

confidence: 99%

A comprehensive survey of mostly textual document segmentation algorithms since 2008

Eskenazi

Gomez‐Krämer

Ogier

2017

Pattern Recognition

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International audienceIn document image analysis, segmentation is the task that identifies the regions of a document. The increasing number of applications of document analysis requires a good knowledge of the available technologies. This survey highlights the variety of the approaches that have been proposed for document image segmentation since 2008. It provides a clear typology of documents and of document image segmentation algorithms. We also discuss the technical limitations of these algorithms, the way they are evaluated and the general trends of the community

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“…In Ref. 2, the steerable pyramid features extracted from pyramid sub-bands serve to locate and classify regions into text and nontext.…”

Section: Introductionmentioning

confidence: 99%

“…In patch-level separation, the entire document is modeled as a MRF, and a MRF-based classi¯cation approach is then used to separate overlapped text into machine-printed text and handwritten text by using pixel-level features. Benjelil et al 2 proposed a system based on a steerable pyramid transform. The features extracted from pyramid sub-bands serve to locate and classify regions into text (either machine-printed or handwritten) and nontext (images, graphics, drawings, or paintings) in some multiscript document images.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Region Projection Method to Discriminate Between Printed and Handwritten Text on Registration Forms

Tan

Lai

Wang

et al. 2015

Int. J. Patt. Recogn. Artif. Intell.

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Techniques to identify printed and handwritten text in scanned documents di®er signi¯cantly. In this paper, we address the question of how to discriminate between each type of writing on registration forms. Registration-form documents consist of various type zones, such as printed text, handwriting, table, image, noise, etc., so segmenting the various zones is a challenge. We adopt herein an approach called \ multiscale-region projection" to identify printed text and handwriting. An important aspect of our approach is the use of multiscale techniques to segment document images. A new set of projection features extracted from each zone is also proposed. The classi¯cation rules are mining and are used to discern printed text and table lines from handwritten text. The proposed system was tested on 11 118 samples in two registrationform-image databases. Some possible measures of e±ciency are computed, and in each case the proposed approach performs better than traditional methods.

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Complex documents images segmentation based on steerable pyramid features

Cited by 13 publications

References 47 publications

Arabic character recognition using a Haar cascade classifier approach (HCC)

Arabic character recognition using a Haar cascade classifier approach (HCC)

A comprehensive survey of mostly textual document segmentation algorithms since 2008

Multiscale Region Projection Method to Discriminate Between Printed and Handwritten Text on Registration Forms

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