Text segmentation and recognition for enhanced image spam detection : an integrated approach
Author(s)
Bibliographic Information
Text segmentation and recognition for enhanced image spam detection : an integrated approach
(EAI/Springer innovations in communication and computing)
Springer, c2021
Available at 3 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
"EAI, Research meets innovation"
Includes bibliographical references and index
Description and Table of Contents
Description
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.
Table of Contents
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.
by "Nielsen BookData"