000			03865nam a22005775i 4500
001			978-3-030-53047-1
003			DE-He213
005			20220801213605.0
007			cr nn 008mamaa
008			200810s2021 sz | s |||| 0|eng d
020			_a9783030530471 _9978-3-030-53047-1
024	7		_a10.1007/978-3-030-53047-1 _2doi
050		4	_aTK5101-5105.9
072		7	_aTJK _2bicssc
072		7	_aTEC041000 _2bisacsh
072		7	_aTJK _2thema
082	0	4	_a621.382 _223
100	1		_aRajalingam, Mallikka. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _933045
245	1	0	_aText Segmentation and Recognition for Enhanced Image Spam Detection _h[electronic resource] : _bAn Integrated Approach / _cby Mallikka Rajalingam.
250			_a1st ed. 2021.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021.
300			_aIX, 114 p. 31 illus., 23 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aEAI/Springer Innovations in Communication and Computing, _x2522-8609
505	0		_aChapter 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			_aThis 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.
650		0	_aTelecommunication. _910437
650		0	_aImage processing—Digital techniques. _931565
650		0	_aComputer vision. _933046
650		0	_aComputational intelligence. _97716
650		0	_aAlgorithms. _93390
650	1	4	_aCommunications Engineering, Networks. _931570
650	2	4	_aComputer Imaging, Vision, Pattern Recognition and Graphics. _931569
650	2	4	_aComputational Intelligence. _97716
650	2	4	_aAlgorithms. _93390
710	2		_aSpringerLink (Online service) _933047
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030530464
776	0	8	_iPrinted edition: _z9783030530488
776	0	8	_iPrinted edition: _z9783030530495
830		0	_aEAI/Springer Innovations in Communication and Computing, _x2522-8609 _933048
856	4	0	_uhttps://doi.org/10.1007/978-3-030-53047-1
912			_aZDB-2-ENG
912			_aZDB-2-SXE
942			_cEBK
999			_c75361 _d75361