Affixal Approach versus Analytical Approach for Off-Line Arabic Decomposable Vocabulary Recognition

Kanoun, Slim; Slimane, Fouad; Guesmi, Hanene; Ingold, Rolf; Alimi, Adel M.; Hennebert, Jean

doi:10.1109/icdar.2009.264

Cited by 9 publications

(5 citation statements)

References 8 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 second used the Levenshtein distance algorithm to test the two Arabic OCR applications. The first one is a state-of-the-art application that generated by Kanoun et al [7][8][9][10] using affixal approach, which is guided by the structural properties of Arabic. The other one is the Readiris Pro version 10 commercial application [54].…”

Section: Testing the Classifiersmentioning

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: Testing the Classifiersmentioning

confidence: 99%

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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“…To generate the lexicon, different Arabic books such as Albukhala of Gahiz 2 and The Muqaddimah-An introduction to the history of Ibn Khaldun 3 were parsed. A collection of Arabic newspaper articles were also taken from the Internet as well as a large lexicon file produced by [15]. This parsing procedure totalled 113,284 single different Arabic words, leading to a pretty good coverage of the Arabic words from different disciplines, e.g.…”

Section: Corpusmentioning

confidence: 99%

“…Non-decomposable words are formed by country/town/village names, Arabic proper names, general names, Arabic prepositions, etc., whereas decomposable words are generated from root Arabic verbs using Arabic schemes [15]. To generate the lexicon, different Arabic books such as Albukhala of Gahiz 2 and The Muqaddimah-An introduction to the history of Ibn Khaldun 3 were parsed.…”

Section: Corpusmentioning

confidence: 99%

Benchmarking Strategy for Arabic Screen-Rendered Word Recognition

Slimane

Kanoun

Hennebert

et al. 2012

Guide to OCR for Arabic Scripts

Self Cite

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This chapter presents a new benchmarking strategy for Arabic screenbased word recognition. Firstly, we report on the creation of the new APTI (Arabic Printed Text Image) database. This database is a large-scale benchmarking of open-vocabulary, multi-font, multi-size and multi-style word recognition systems in Arabic. Such systems take as input a text image and compute as output a character string corresponding to the text included in the image. The challenges that are addressed by the database are in the variability of the sizes, fonts and styles used to generate the images. A focus is also given on low resolution images where anti-aliasing is generating noise on the characters being recognized. The database contains 45,313,600 single word images totalling more than 250 million characters. Ground truth annotation is provided for each image from an XML file. The annotation includes the number of characters, the number of pieces of Arabic words (PAWs), the sequence of characters, the size, the style, the font used to generate each image, etc. Secondly, we describe the Arabic Recognition Competition: Multi-Font Multi-Size Digitally Represented Text held in the

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