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Cursive script text recognition in n...

Ahmed, Saad Bin.

Cursive script text recognition in natural scene imagesarabic text complexities /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Cursive script text recognition in natural scene imagesby 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.
Contained By:	Springer eBooks
Subject:	Optical character recognition.
Online resource:	https://doi.org/10.1007/978-981-15-1297-1
ISBN:	9789811512971$q(electronic bk.)

Cursive script text recognition in natural scene imagesarabic 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$q(electronic bk.)

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

568947
Optical character recognition.

LC Class. No.: TA1640 / .A45 2020

Dewey Class. No.: 006.424

Cursive script text recognition in natural scene imagesarabic text complexities /
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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.
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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 568947
650 0 $a Natural language processing (Computer science) $3 200539
650 1 4 $a Information Storage and Retrieval. $3 274190
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Natural Language Processing (NLP) $3 826373
700 1 $a Razzak, Muhammad Imran. $3 861500
700 1 $a Yusof, Rubiyah. $3 861501
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