Name: Text Segmentation and Recognition for Enhanced Image Spam Detection: An Integrated Approach (en Inglés)
Price: 539.34 PEN
Availability: InStock
Author: Rajalingam, Mallikka
ISBN: 9783030530495

portada Text Segmentation and Recognition for Enhanced Image Spam Detection: An Integrated Approach (en Inglés)

Formato

Libro Físico

Autor

Mallikka Rajalingam

Editorial

Springer

Idioma

Inglés

N° páginas

114

Encuadernación

Tapa Blanda

Dimensiones

23.4 x 15.6 x 0.7 cm

Peso

0.19 kg.

ISBN13

9783030530495

Categorías

Ingeniería: general
Ingeniería de las comunicaciones y las telecomunicaciones
Reconocimiento de formas
Visión por ordenador

Text Segmentation and Recognition for Enhanced Image Spam Detection: An Integrated Approach (en Inglés)

Mallikka Rajalingam (Autor) · Springer · Tapa Blanda

Libro Nuevo

S/ 539,34

S/ 1.078,69

Ahorras: S/ 539,34

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Envío normal

Origen: Estados Unidos (Costos de importación incluídos en el precio)

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Reseña del libro "Text Segmentation and Recognition for Enhanced Image Spam Detection: An Integrated Approach (en Inglés)"

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.