Although readers of detective fiction ordinarily expect to learn the mystery's solution at the end, there is another kind of detective story—the history of which encompasses writers as diverse as Poe, Borges, Robbe-Grillet, Auster, and Stephen King—that ends with a question rather than an answer. The detective not only fails to solve the crime, but also confronts insoluble mysteries of interpretation and identity. As the contributors to Detecting Texts contend, such stories belong to a distinct genre, the "metaphysical detective story," in which the detective hero's inability to interpret the mystery inevitably casts doubt on the reader's similar attempt to make sense of the text and the world. Detecting Texts includes an introduction by the editors that defines the metaphysical detective story and traces its history from Poe's classic tales to today's postmodernist experiments. In addition to the editors, contributors include Stephen Bernstein, Joel Black, John T. Irwin, Jeffrey T. Nealon, and others.

This book presents a systematic introduction to the latest developments in video text detection. Opening with a discussion of the underlying theory and a brief history of video text detection, the text proceeds to cover pre-processing and post-processing techniques, character segmentation and recognition, identification of non-English scripts, techniques for multi-modal analysis and performance evaluation. The detection of text from both natural video scenes and artificially inserted captions is examined. Various applications of the technology are also reviewed, from license plate recognition and road navigation assistance, to sports analysis and video advertising systems. Features: explains the fundamental theory in a succinct manner, supplemented with references for further reading; highlights practical techniques to help the reader understand and develop their own video text detection systems and applications; serves as an easy-to-navigate reference, presenting the material in self-contained chapters.

Identifying and stopping the dissemination of fabricated news, hate speech, or deceptive information camouflaged as legitimate news poses a significant technological hurdle. This book presents emergent methodologies and technological approaches of natural language processing through machine learning for counteracting the spread of fake news and hate speech on social media platforms. • Covers various approaches, algorithms, and methodologies for fake news and hate speech detection. • Explains the automatic detection and prevention of fake news and hate speech through paralinguistic clues on social media using artificial intelligence. • Discusses the application of machine learning models to learn linguistic characteristics of hate speech over social media platforms. • Emphasizes the role of multilingual and multimodal processing to detect fake news. • Includes research on different optimization techniques, case studies on the identification, prevention, and social impact of fake news, and GitHub repository links to aid understanding. The text is for professionals and scholars of various disciplines interested in fake news and hate speech detection.

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.

This book constitutes the refereed proceedings of the 5th Multidisciplinary International Symposium on Disinformation in Open Online Media, MISDOOM 2023, which was held in Amsterdam, The Netherlands, during November 21–22, 2023. The 13 full papers presented in this book were carefully reviewed and selected from 19 submissions. The papers focus on misinformation, disinformation, hate speech, disinformation campaigns, social network analysis, large language models, generative AI, and multi-modal embeddings.

This book is a printed edition of the Special Issue "Document Image Processing" that was published in J. Imaging

The purpose of this book is to introduce the reader to the basic theory of signal detection and estimation. It is assumed that the reader has a working knowledge of applied probabil ity and random processes such as that taught in a typical first-semester graduate engineering course on these subjects. This material is covered, for example, in the book by Wong (1983) in this series. More advanced concepts in these areas are introduced where needed, primarily in Chapters VI and VII, where continuous-time problems are treated. This book is adapted from a one-semester, second-tier graduate course taught at the University of Illinois. However, this material can also be used for a shorter or first-tier course by restricting coverage to Chapters I through V, which for the most part can be read with a background of only the basics of applied probability, including random vectors and conditional expectations. Sufficient background for the latter option is given for exam pIe in the book by Thomas (1986), also in this series.

The two volume set, consisting of LNCS 7728 and 7729, contains the carefully reviewed and selected papers presented at the nine workshops that were held in conjunction with the 11th Asian Conference on Computer Vision, ACCV 2012, in Daejeon, South Korea, in November 2012. From a total of 310 papers submitted, 78 were selected for presentation. LNCS 7728 contains the papers selected for the International Workshop on Computer Vision with Local Binary Pattern Variants, the Workshop on Computational Photography and Low-Level Vision, the Workshop on Developer-Centered Computer Vision, and the Workshop on Background Models Challenge. LNCS 7729 contains the papers selected for the Workshop on e-Heritage, the Workshop on Color Depth Fusion in Computer Vision, the Workshop on Face Analysis, the Workshop on Detection and Tracking in Challenging Environments, and the International Workshop on Intelligent Mobile Vision.