Text Segmentation and Recognition for Enhanced Image Spam Detection: An Integrated Approach

ISBN-13: 9783030530464 / Angielski / Twarda / 2020 / 114 str.

Mallikka Rajalingam

Spis treści

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. 

O autorze

Mallikka Rajalingam received her M.Sc Information Technology from Bharathidasan University, Tiruchirappalli, India in 2005, M.Phil Computer Science from Madurai Kamaraj University, Madurai, India in 2008, M.Tech Computer Science & Engineering from SASTRA University, Thanjavur, India in 2009. She worked as a Research Officer (RO) at School of Computer Science, Universiti Sains Malaysia (USM), Malaysia. She is currently pursuing the Ph.D. degree at the Department of Computer Science & Engineering, Bharathidasan University, Trichy, India. She published 13 International journals, 07 International conferences and 09 workshops during her research. Her research interests include image processing, computer vision, pattern recognition, character recognition, document image analysis, text analysis and multimedia networking.

Blurb

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.