Last Teen Pixels for Arabic Font Size and Style Recognition

Arabic printed script segmentation and recognition techniques change from font to other i.e. each font has particular properties calligraphic and structural which differ with other. Majority of segmentation system suffer in word or sub word segmentation into characters because they consider one algo...

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Autores principales:	Abdelouahed Ait Ider, Said Nouri, Abdelkrim Maarir
Formato:	article
Lenguaje:	EN
Publicado:	International Association of Online Engineering (IAOE) 2021
Materias:	word sub-word characters APTI last teen pixels Pixels Continuity (PC) Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/1e2c3bba80e149fd8663f73c5eb2de52
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id	oai:doaj.org-article:1e2c3bba80e149fd8663f73c5eb2de52
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spelling	oai:doaj.org-article:1e2c3bba80e149fd8663f73c5eb2de522021-11-30T07:59:35ZLast Teen Pixels for Arabic Font Size and Style Recognition10.3991/ijoe.v17i12.250652626-8493https://doaj.org/article/1e2c3bba80e149fd8663f73c5eb2de522021-11-01T00:00:00Zhttps://www.online-journals.org/index.php/i-joe/article/view/25065https://doaj.org/toc/2626-8493Arabic printed script segmentation and recognition techniques change from font to other i.e. each font has particular properties calligraphic and structural which differ with other. Majority of segmentation system suffer in word or sub word segmentation into characters because they consider one algorithm to segment all kind of Arabic printed font, style and size. The goal of this work is to prepare a system of word or sub word Optical Font Arabic Recognition (OFAR) for different font size and style of Arabic printed script, in order to integrate it in global Arabic Optical Character Recognition (AOCR) to choose preferred and good segmentation algorithm. APTI database was used to extract last ten pixels for each word or sub word to build new database of last 10 pixels for each word; OFAR is based upon this new database and our extraction approach called Pixels Continuity (PC) algorithm in different matrix direction and some histogram statistics to extract 20 features. Three KNN classifiers with K=5 and three different distances using Cityblock, Euclidean and Correlation based upon majority-vote are used to evaluate the system robustness. This classifier is compared in the first time with Back propagation Neural Network and Steerable Pyramid (SP) algorithm to re cognize three font families, then in the second time with Gaussian Mixture Models (GMMs) to recognize font and size. The average recognition results obtained was 99.55% about font and size and 98.17% for font, size and style recognition.Abdelouahed Ait IderSaid NouriAbdelkrim MaarirInternational Association of Online Engineering (IAOE)articlewordsub-wordcharactersAPTIlast teen pixelsPixels Continuity (PC)Computer applications to medicine. Medical informaticsR858-859.7ENInternational Journal of Online and Biomedical Engineering, Vol 17, Iss 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	word sub-word characters APTI last teen pixels Pixels Continuity (PC) Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	word sub-word characters APTI last teen pixels Pixels Continuity (PC) Computer applications to medicine. Medical informatics R858-859.7 Abdelouahed Ait Ider Said Nouri Abdelkrim Maarir Last Teen Pixels for Arabic Font Size and Style Recognition
description	Arabic printed script segmentation and recognition techniques change from font to other i.e. each font has particular properties calligraphic and structural which differ with other. Majority of segmentation system suffer in word or sub word segmentation into characters because they consider one algorithm to segment all kind of Arabic printed font, style and size. The goal of this work is to prepare a system of word or sub word Optical Font Arabic Recognition (OFAR) for different font size and style of Arabic printed script, in order to integrate it in global Arabic Optical Character Recognition (AOCR) to choose preferred and good segmentation algorithm. APTI database was used to extract last ten pixels for each word or sub word to build new database of last 10 pixels for each word; OFAR is based upon this new database and our extraction approach called Pixels Continuity (PC) algorithm in different matrix direction and some histogram statistics to extract 20 features. Three KNN classifiers with K=5 and three different distances using Cityblock, Euclidean and Correlation based upon majority-vote are used to evaluate the system robustness. This classifier is compared in the first time with Back propagation Neural Network and Steerable Pyramid (SP) algorithm to re cognize three font families, then in the second time with Gaussian Mixture Models (GMMs) to recognize font and size. The average recognition results obtained was 99.55% about font and size and 98.17% for font, size and style recognition.
format	article
author	Abdelouahed Ait Ider Said Nouri Abdelkrim Maarir
author_facet	Abdelouahed Ait Ider Said Nouri Abdelkrim Maarir
author_sort	Abdelouahed Ait Ider
title	Last Teen Pixels for Arabic Font Size and Style Recognition
title_short	Last Teen Pixels for Arabic Font Size and Style Recognition
title_full	Last Teen Pixels for Arabic Font Size and Style Recognition
title_fullStr	Last Teen Pixels for Arabic Font Size and Style Recognition
title_full_unstemmed	Last Teen Pixels for Arabic Font Size and Style Recognition
title_sort	last teen pixels for arabic font size and style recognition
publisher	International Association of Online Engineering (IAOE)
publishDate	2021
url	https://doaj.org/article/1e2c3bba80e149fd8663f73c5eb2de52
work_keys_str_mv	AT abdelouahedaitider lastteenpixelsforarabicfontsizeandstylerecognition AT saidnouri lastteenpixelsforarabicfontsizeandstylerecognition AT abdelkrimmaarir lastteenpixelsforarabicfontsizeandstylerecognition
_version_	1718406729958424576

Last Teen Pixels for Arabic Font Size and Style Recognition

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