A Printed PAW Image Database of Arabic Language for Document Analysis and Recognition

Bataineh, Bilal

doi:10.5614/itbj.ict.res.appl.2017.11.2.6

Jurnal SINTA 2

A Printed PAW Image Database of Arabic Language for Document Analysis and Recognition

Bataineh, Bilal^{Umm al-Qura University}

Journal of ICT Research and Applications (JICTRA) · Vol. 11 No. 2 (2017) · pp. 200–212

DOI: 10.5614/itbj.ict.res.appl.2017.11.2.6
ISSN: 2338-5499, 2337-5787
Published: 2017-08-31
Publisher: DRI - ITB
Language: eng

Abstract

Document image analysis and recognition are important topics in the field of artificial intelligence. In this context, the availability of a database with good script samples is an important requirement for machine-learning processes. For Latin and Asian languages many suitable databases exist. However, there is a shortage of databases with Arabic samples. In this work, a new database of printed Arabic text is introduced. The new concept of collecting sub-words (PAWs) instead of words or individual character samples was adopted. These PAWs constitute all words in the Arabic language. The collected database consists of 83,056 images of PAWs extracted from approximately 550,000 different words. Each sample is presented in the database in five font types: Thuluth, Naskh, Andalusi, Typing Machine, and Kufi. In total, the database consists of 415,280 images. Moreover, ground truth information is included with each PAW image to describe its occurrence number, occurrence frequency, positions and the shapes of the characters. This paper presents a statistical analysis of the frequency of each PAW in the Arabic language.

Keywords

Collaboration network of the authors

7 authors · 16 co-authorship links · 1 across different organisations

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National University of Malaysia
Umm al-Qura University
Affiliation unknown

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Same organisation
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Co-author	Organisation	Joint articles
Tarik Abu-Ain	National University of Malaysia	2
Siti Norul Huda Sheikh Abdullah	National University of Malaysia	2
Khairuddin Omar	National University of Malaysia	2
Ashraf Abu-Ein	Affiliation unknown	1
Waleed Abdel karim Abu-ain	National University of Malaysia	1
Mohd Khaled Yousef Shambour	Umm al-Qura University	1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">A Printed PAW Image Database of Arabic Language for Document Analysis and Recognition</dc:title>
	<dc:creator>Bataineh, Bilal</dc:creator>
	<dc:subject xml:lang="en-US">Arabic language</dc:subject>
	<dc:subject xml:lang="en-US">database</dc:subject>
	<dc:subject xml:lang="en-US">document images</dc:subject>
	<dc:subject xml:lang="en-US">information retrieval</dc:subject>
	<dc:subject xml:lang="en-US">OCR</dc:subject>
	<dc:subject xml:lang="en-US">PAWs.</dc:subject>
	<dc:description xml:lang="en-US">Document image analysis and recognition are important topics in the field of artificial intelligence. In this context, the availability of a database with good script samples is an important requirement for machine-learning processes. For Latin and Asian languages many suitable databases exist. However, there is a shortage of databases with Arabic samples. In this work, a new database of printed Arabic text is introduced. The new concept of collecting sub-words (PAWs) instead of words or individual character samples was adopted. These PAWs constitute all words in the Arabic language. The collected database consists of 83,056 images of PAWs extracted from approximately 550,000 different words. Each sample is presented in the database in five font types: Thuluth, Naskh, Andalusi, Typing Machine, and Kufi. In total, the database consists of 415,280 images. Moreover, ground truth information is included with each PAW image to describe its occurrence number, occurrence frequency, positions and the shapes of the characters. This paper presents a statistical analysis of the frequency of each PAW in the Arabic language.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2017-08-31</dc:date>
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	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 11 No. 2 (2017); 200-212</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2017.11.2</dc:source>
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