Text Segmentation and Recognition for Enhanced Image Spam Detection (Record no. 75361)
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000 -LEADER | |
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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 |
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-- | 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 |
912 ## - | |
-- | ZDB-2-SXE |
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