Text Segmentation and Recognition for Enhanced Image Spam Detection (Record no. 75361)

000 -LEADER
fixed length control field 03865nam a22005775i 4500
001 - CONTROL NUMBER
control field 978-3-030-53047-1
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220801213605.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200810s2021 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783030530471
-- 978-3-030-53047-1
082 04 - CLASSIFICATION NUMBER
Call Number 621.382
100 1# - AUTHOR NAME
Author Rajalingam, Mallikka.
245 10 - TITLE STATEMENT
Title Text Segmentation and Recognition for Enhanced Image Spam Detection
Sub Title An Integrated Approach /
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2021.
300 ## - PHYSICAL DESCRIPTION
Number of Pages IX, 114 p. 31 illus., 23 illus. in color.
490 1# - SERIES STATEMENT
Series statement EAI/Springer Innovations in Communication and Computing,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 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 ## - SUMMARY, ETC.
Summary, etc 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.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-030-53047-1
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2021.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Telecommunication.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Image processing—Digital techniques.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer vision.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Algorithms.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Communications Engineering, Networks.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer Imaging, Vision, Pattern Recognition and Graphics.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Algorithms.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2522-8609
912 ## -
-- ZDB-2-ENG
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-- ZDB-2-SXE

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