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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 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Cursive script text recognition in natural scene images/ by Saad Bin Ahmed, Muhammad Imran Razzak, Rubiyah Yusof.
Reminder of title:	arabic text complexities /
Author:	Ahmed, Saad Bin.
other author:	Razzak, Muhammad Imran.
Published:	Singapore :Springer Singapore : : 2020.,
Description:	xv, 111 p. :ill., digital ;24 cm.
[NT 15003449]:	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
Subject:	Optical character recognition. -
Online resource:	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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100 1 $a Ahmed, Saad Bin. $3 3445122
245 1 0 $a Cursive script text recognition in natural scene images $h [electronic resource] : $b arabic text complexities / $c by Saad Bin Ahmed, Muhammad Imran Razzak, Rubiyah Yusof.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a xv, 111 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Optical character recognition. $3 1619729
650 0 $a Natural language processing (Computer science) $3 565309
650 1 4 $a Information Storage and Retrieval. $3 761906
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Natural Language Processing (NLP) $3 3381674
700 1 $a Razzak, Muhammad Imran. $3 3445123
700 1 $a Yusof, Rubiyah. $3 3445124
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-981-15-1297-1
950 $a Computer Science (Springer-11645)