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Cursive script text recognition in n...

Ahmed, Saad Bin.

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Cursive script text recognition in natural scene images = arabic text complexities /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Cursive script text recognition in natural scene images/ by Saad Bin Ahmed, Muhammad Imran Razzak, Rubiyah Yusof.
其他題名:	arabic text complexities /
作者:	Ahmed, Saad Bin.
其他作者:	Razzak, Muhammad Imran.
出版者:	Singapore :Springer Singapore : : 2020.,
面頁冊數:	xv, 111 p. :ill., digital ;24 cm.
內容註:	Foundations of Cursive Scene Text -- Text in a wild and it's Challenges -- 3 Arabic Scene Text Acquisition and Statistics -- Methods and Algorithm -- Progress in Cursive Wild Text Recognition -- Conclusion and Future Work.
Contained By:	Springer eBooks
標題:	Optical character recognition. -
電子資源:	https://doi.org/10.1007/978-981-15-1297-1
ISBN:	9789811512971

Cursive script text recognition in natural scene images = arabic text complexities /
Ahmed, Saad Bin.

Cursive script text recognition in natural scene imagesarabic text complexities /[electronic resource] :by Saad Bin Ahmed, Muhammad Imran Razzak, Rubiyah Yusof. - Singapore :Springer Singapore :2020. - xv, 111 p. :ill., digital ;24 cm.

Foundations of Cursive Scene Text -- Text in a wild and it's Challenges -- 3 Arabic Scene Text Acquisition and Statistics -- Methods and Algorithm -- Progress in Cursive Wild Text Recognition -- Conclusion and Future Work.

This book offers a broad and structured overview of the state-of-the-art methods that could be applied for context-dependent languages like Arabic. It also provides guidelines on how to deal with Arabic scene data that appeared in an uncontrolled environment impacted by different font size, font styles, image resolution, and opacity of text. Being an intrinsic script, Arabic and Arabic-like languages attract attention from research community. There are a number of challenges associated with the detection and recognition of Arabic text from natural images. This book discusses these challenges and open problems and also provides insights into the complexities and issues that researchers encounter in the context of Arabic or Arabic-like text recognition in natural and document images. It sheds light on fundamental questions, such as a) How the complexity of Arabic as a cursive scripts can be demonstrated b) What the structure of Arabic text is and how to consider the features from a given text and c) What guidelines should be followed to address the context learning ability of classifiers existing in machine learning.

ISBN: 9789811512971

Standard No.: 10.1007/978-981-15-1297-1doiSubjects--Topical Terms:

1619729
Optical character recognition.

LC Class. No.: TA1640 / .A45 2020

Dewey Class. No.: 006.424

Cursive script text recognition in natural scene images = arabic text complexities /
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505 0 $a Foundations of Cursive Scene Text -- Text in a wild and it's Challenges -- 3 Arabic Scene Text Acquisition and Statistics -- Methods and Algorithm -- Progress in Cursive Wild Text Recognition -- Conclusion and Future Work.
520 $a This book offers a broad and structured overview of the state-of-the-art methods that could be applied for context-dependent languages like Arabic. It also provides guidelines on how to deal with Arabic scene data that appeared in an uncontrolled environment impacted by different font size, font styles, image resolution, and opacity of text. Being an intrinsic script, Arabic and Arabic-like languages attract attention from research community. There are a number of challenges associated with the detection and recognition of Arabic text from natural images. This book discusses these challenges and open problems and also provides insights into the complexities and issues that researchers encounter in the context of Arabic or Arabic-like text recognition in natural and document images. It sheds light on fundamental questions, such as a) How the complexity of Arabic as a cursive scripts can be demonstrated b) What the structure of Arabic text is and how to consider the features from a given text and c) What guidelines should be followed to address the context learning ability of classifiers existing in machine learning.
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