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Text segmentation and recognition fo...

Rajalingam, Mallikka.

Text segmentation and recognition for enhanced image spam detectionan integrated approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Text segmentation and recognition for enhanced image spam detectionby Mallikka Rajalingam.
Reminder of title:	an integrated approach /
Author:	Rajalingam, Mallikka.
Published:	Cham :Springer International Publishing :2021.
Description:	ix, 114 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Image segmentation.
Online resource:	https://doi.org/10.1007/978-3-030-53047-1
ISBN:	9783030530471$q(electronic bk.)

Text segmentation and recognition for enhanced image spam detectionan integrated approach /
Rajalingam, Mallikka.

Text segmentation and recognition for enhanced image spam detectionan integrated approach /[electronic resource] :by Mallikka Rajalingam. - Cham :Springer International Publishing :2021. - ix, 114 p. :ill., digital ;24 cm. - EAI/Springer innovations in communication and computing,2522-8595. - EAI/Springer innovations in communication and computing..

Chapter 1. Introduction -- Chapter 2. Review of Literature -- Chapter 3. Methodology -- Chapter 4. Character Segmentation -- Chapter 5. Character Recognition -- Chapter 6. Classification/Feature Extraction Using SVM and KNN Classifier -- Chapter 7. Experimentation and Result discussion -- Chapter 8. Conclusion.

This book discusses email spam detection and its challenges such as text classification and categorization. The book proposes an efficient spam detection technique that is a combination of Character Segmentation and Recognition and Classification (CSRC) The author describes how this can detect whether an email (text and image based) is a spam mail or not. The book presents four solutions: first, to extract the text character from the image by segmentation process which includes a combination of Discrete Wavelet Transform (DWT) and skew detection. Second, text characters are via text recognition and visual feature extraction approach which relies on contour analysis with improved Local Binary Pattern (LBP) Third, extracted text features are classified using improvised K-Nearest Neighbor search (KNN) and Support Vector Machine (SVM) Fourth, the performance of the proposed method is validated by the measure of metric named as sensitivity, specificity, precision, recall, F-measure, accuracy, error rate and correct rate. Presents solutions to email spam detection and discusses its challenges such as text classification and categorization; Analyzes the proposed techniques' performance using precision, F-measure, recall and accuracy; Evaluates the limitations of the proposed research thereby recommending future research.

ISBN: 9783030530471$q(electronic bk.)

Standard No.: 10.1007/978-3-030-53047-1doiSubjects--Topical Terms:

710586
Image segmentation.

LC Class. No.: TA1638.4 / .R35 2021

Dewey Class. No.: 006.6

Text segmentation and recognition for enhanced image spam detectionan integrated approach /
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100 1 $a Rajalingam, Mallikka. $3 887056
245 1 0 $a Text segmentation and recognition for enhanced image spam detection $h [electronic resource] : $b an integrated approach / $c by Mallikka Rajalingam.
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490 1 $a EAI/Springer innovations in communication and computing, $x 2522-8595
505 0 $a Chapter 1. Introduction -- Chapter 2. Review of Literature -- Chapter 3. Methodology -- Chapter 4. Character Segmentation -- Chapter 5. Character Recognition -- Chapter 6. Classification/Feature Extraction Using SVM and KNN Classifier -- Chapter 7. Experimentation and Result discussion -- Chapter 8. Conclusion.
520 $a This book discusses email spam detection and its challenges such as text classification and categorization. The book proposes an efficient spam detection technique that is a combination of Character Segmentation and Recognition and Classification (CSRC) The author describes how this can detect whether an email (text and image based) is a spam mail or not. The book presents four solutions: first, to extract the text character from the image by segmentation process which includes a combination of Discrete Wavelet Transform (DWT) and skew detection. Second, text characters are via text recognition and visual feature extraction approach which relies on contour analysis with improved Local Binary Pattern (LBP) Third, extracted text features are classified using improvised K-Nearest Neighbor search (KNN) and Support Vector Machine (SVM) Fourth, the performance of the proposed method is validated by the measure of metric named as sensitivity, specificity, precision, recall, F-measure, accuracy, error rate and correct rate. Presents solutions to email spam detection and discusses its challenges such as text classification and categorization; Analyzes the proposed techniques' performance using precision, F-measure, recall and accuracy; Evaluates the limitations of the proposed research thereby recommending future research.
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650 1 4 $a Communications Engineering, Networks. $3 273745
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