Download or read book Probabilistic Indexing for Information Search and Retrieval in Large Collections of Handwritten Text Images written by and published by Springer Nature. This book was released on 2024 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive presentation of a recently introduced framework, named "probabilistic indexing" (PrIx), for searching text in large collections of document images and other related applications. It fosters the development of new search engines for effective information retrieval from manuscripts which, however, lack the electronic text (transcripts) that would typically be required for such search and retrieval tasks. The book is structured into 11 chapters and three appendices. The first two chapters briefly outline the necessary fundamentals and state of the art in pattern recognition, statistical decision theory, and handwritten text recognition. Chapter 3 presents approaches for indexing (as opposed to spotting) each region of a handwritten text image which is likely to contain a word. Next, Chapter 4 describes models adopted for handwritten text in images, namely hidden Markov models, convolutional and recurrent neural networks and language models, and provides full details of weighted finite-state transducer (WFST) concepts and methods, needed in further chapters of the book. Chapter 5 explains the set of techniques and algorithms developed to generate image probabilistic indexes which allow for fast search and retrieval of textual information in the indexed images. Chapter 6 then presents experimental evaluations of the proposed framework and algorithms on different traditional benchmark datasets and compares them with other approaches, while Chapter 7 reviews the most popular keyword-spotting approaches. Chapter 8 explains how PrIx can support classical free-text search tools, while Chapter 9 presents new methods that use PrIx not only for searching, but also to deal with text analytics and other related natural language processing and information extraction tasks. Chapter 10 shows how the proposed solutions can be used to effectively index very large collections of handwritten document images, before Chapter 11 eventually summarizes the book and suggests promising lines of future research. The appendices detail the necessary mathematical foundations for the work and presents details of the text image collections and datasets used in the experiments throughout the book. This book is written for researchers and (post-)graduate students in pattern recognition and information retrieval. It will also be of interest to people in areas like history, criminology, or psychology who need technical support to evaluate, understand or decode historical or contemporary handwritten text.

Download or read book Document Analysis Systems written by Seiichi Uchida and published by Springer Nature. This book was released on 2022-05-17 with total page 795 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 15th IAPR International Workshop on Document Analysis Systems, DAS 2022, held in La Rochelle, France, in May 2022. The full papers presented were carefully reviewed and selected from numerous submissions addressing key techniques of document analysis.

Download or read book Pattern Recognition and Image Analysis written by Armando J. Pinho and published by Springer Nature. This book was released on 2022-04-25 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2022, held in Aveiro, Portugal, in May 2022. The 54 papers accepted for these proceedings were carefully reviewed and selected from 72 submissions. They deal with document analysis; medical image processing; biometrics; pattern recognition and machine learning; computer vision; and other applications.

Download or read book Pattern Recognition and Image Analysis written by Aythami Morales and published by Springer Nature. This book was released on 2019-09-21 with total page 534 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 2-volume set constitutes the refereed proceedings of the 9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019, held in Madrid, Spain, in July 2019. The 99 papers in these volumes were carefully reviewed and selected from 137 submissions. They are organized in topical sections named: Part I: best ranked papers; machine learning; pattern recognition; image processing and representation. Part II: biometrics; handwriting and document analysis; other applications.

Download or read book Content based Handwritten Document Indexing and Retrieval written by and published by . This book was released on 2008 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information retrieval on textual data has been well studied and its applications (such as web searching) have become ubiquitous in our daily lives. However content-based image retrieval on handwritten document collections still remains a challenging problem. Here "content-based" means that the search will analyze the actual content of the images, instead of merely the metadata. In the context of handwritten documents, the word "content" might refer different things, such as writing style, shape of words and characters, or the truth of the writing. Accordingly, two different types of retrieval can be performed: "query by example" and semantic (or "query by text") retrieval. While both of them have their own applications in the real world, the second one is more intuitive and user-friendly, since it uses not only the low level underlying computational features, but also the understanding of documents. This work explores several automatic techniques to do both types of retrieval upon handwritten document collections. These techniques are three-fold: (i) indexing, (ii) "query by example" retrieval and (iii) "query by text" retrieval. For indexing, we focus on the problem of word segmentation and transcript mapping. Word segmentation is the task of segmenting text line images into word image, which is one of the most important preprocessing steps in order to perform any word level analysis or recognition. We propose the use of neural network with a new set of global and local features to make the classification between inter-word and intra-word gaps. The transcript mapping problem is an alignment problem between the handwritten document image and its transcript. It is not a trivial task simply because the word segmentation algorithm is error prone. A recognition based dynamic programming algorithm is proposed to solve this problem. It is also shown to improve the accuracy of automatic word segmentation. In "query by example" retrieval, the query can be either a full page document or a single word image. For the document level retrieval, a statistical model is learned to determine whether the writing styles of two documents are similar or not. Gamma and Gaussian distributions are used for the modeling. Word level retrieval is performed by a feature based similarity search algorithm. For each word image, a 1024-bit binary feature vector is extracted for this purpose. "Query by text" retrieval is a more challenging task because word level segmentation is error prone and word recognition with large lexicon size is still an unsolved problem. The current solution for this problem is to manually annotate the collection, which is costly. By taking the idea from machine translation in textual information retrieval, we propose a statistical approach for word recognition and use the probabilistic annotation results to do language model retrieval on handwritten documents. For all these approaches, their performances are empirically compared on several test collections. The main contributions of this work are a detailed examination of different levels of content-based image retrieval for handwritten documents, and the development of a retrieval system that allows either image or text queries. The new word segmentation method shows an improved performance over a previous method and is useful in forensic document analysis. In addition, a large handwriting database of 3824 pages (about 573,600 labeled words) was created using the proposed transcript-mapping algorithm. This database was used predominantly in this dissertation and it serves as a useful resource for future handwriting analysis and recognition research.

Download or read book Pattern Recognition and Image Analysis written by Luís A. Alexandre and published by Springer. This book was released on 2017-06-08 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2017, held in Faro, Portugal, in June 2017. The 60 regular papers presented in this volume were carefully reviewed and selected from 86 submissions. They are organized in topical sections named: Pattern Recognition and Machine Learning; Computer Vision; Image and Signal Processing; Medical Image; and Applications.

Download or read book Handwritten Historical Document Analysis Recognition And Retrieval State Of The Art And Future Trends written by Andreas Fischer and published by World Scientific. This book was released on 2020-11-11 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, libraries and archives all around the world have increased their efforts to digitize historical manuscripts. To integrate the manuscripts into digital libraries, pattern recognition and machine learning methods are needed to extract and index the contents of the scanned images.The unique compendium describes the outcome of the HisDoc research project, a pioneering attempt to study the whole processing chain of layout analysis, handwriting recognition, and retrieval of historical manuscripts. This description is complemented with an overview of other related research projects, in order to convey the current state of the art in the field and outline future trends.This must-have volume is a relevant reference work for librarians, archivists and computer scientists.

Download or read book Computer Vision and Image Processing written by Balasubramanian Raman and published by Springer Nature. This book was released on 2022-07-23 with total page 598 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set (CCIS 1567-1568) constitutes the refereed proceedings of the 6h International Conference on Computer Vision and Image Processing, CVIP 2021, held in Rupnagar, India, in December 2021. The 70 full papers and 20 short papers were carefully reviewed and selected from the 260 submissions. The papers present recent research on such topics as biometrics, forensics, content protection, image enhancement/super-resolution/restoration, motion and tracking, image or video retrieval, image, image/video processing for autonomous vehicles, video scene understanding, human-computer interaction, document image analysis, face, iris, emotion, sign language and gesture recognition, 3D image/video processing, action and event detection/recognition, medical image and video analysis, vision-based human GAIT analysis, remote sensing, and more.

Download or read book Linking Theory and Practice of Digital Libraries written by Gerd Berget and published by Springer Nature. This book was released on 2021-09-06 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 25th International Conference on Theory and Practice of Digital Libraries, TPDL 2021, held in September 2021. Due to COVID-10 pandemic the conference was held virtually. The 10 full papers, 3 short papers and 13 other papers presented were carefully reviewed and selected from 53 submissions. TPDL 2021 attempts to facilitate establishing connections and convergences between diverse research communities such as Digital Humanities, Information Sciences and others that could benefit from ecosystems offered by digital libraries and repositories. This edition of TPDL was held under the general theme of “Linking Theory and Practice”. The papers are organized in topical sections as follows: Document and Text Analysis; Data Repositories and Archives; Linked Data and Open Data; User Interfaces and Experience.

Download or read book Document Analysis and Recognition ICDAR 2021 written by Josep Lladós and published by Springer Nature. This book was released on 2021-09-04 with total page 878 pages. Available in PDF, EPUB and Kindle. Book excerpt: This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports. The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding.

Download or read book Document Analysis and Recognition ICDAR 2023 written by Gernot A. Fink and published by Springer Nature. This book was released on 2023-08-18 with total page 561 pages. Available in PDF, EPUB and Kindle. Book excerpt: This six-volume set of LNCS 14187, 14188, 14189, 14190, 14191 and 14192 constitutes the refereed proceedings of the 17th International Conference on Document Analysis and Recognition, ICDAR 2023, held in San José, CA, USA, in August 2023. The 53 full papers were carefully reviewed and selected from 316 submissions, and are presented with 101 poster presentations. The papers are organized into the following topical sections: Graphics Recognition, Frontiers in Handwriting Recognition, Document Analysis and Recognition.

Download or read book Document Image Processing written by Ergina Kavallieratou and published by MDPI. This book was released on 2018-10-03 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Document Image Processing" that was published in J. Imaging

Download or read book Indexing and Retrieval of Text Images Using Word Spotting Technique written by Ali Abidi and published by LAP Lambert Academic Publishing. This book was released on 2011-07 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Libraries in South Asia hold huge collections of valuable printed documents in Urdu and it is of interest to digitize these collections to make them more accessible. The unavailability of an OCR for Urdu however limits the concept of a digital Urdu library to scanning of documents only, offering very limited search facility based on manually assigned tags. We address this issue by proposing a word spotting based keyword search method for information retrieval in digitized collections of printed Urdu documents. The proposed method is based on segmentation of Urdu text in to partial words and representing each partial word by a set of features. To search a specific word (or phrase), the user provides a query in the form of an image. Comparing the features of the partial words in the query image with the ones already indexed, the user is provided with a list of documents containing occurrences of the queried word.

Download or read book Introduction to Information Retrieval written by Christopher D. Manning and published by Cambridge University Press. This book was released on 2008-07-07 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.

Download or read book A Generative Theory of Relevance written by Victor Lavrenko and published by Springer Science & Business Media. This book was released on 2008-11-14 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: A modern information retrieval system must have the capability to find, organize and present very different manifestations of information – such as text, pictures, videos or database records – any of which may be of relevance to the user. However, the concept of relevance, while seemingly intuitive, is actually hard to define, and it's even harder to model in a formal way. Lavrenko does not attempt to bring forth a new definition of relevance, nor provide arguments as to why any particular definition might be theoretically superior or more complete. Instead, he takes a widely accepted, albeit somewhat conservative definition, makes several assumptions, and from them develops a new probabilistic model that explicitly captures that notion of relevance. With this book, he makes two major contributions to the field of information retrieval: first, a new way to look at topical relevance, complementing the two dominant models, i.e., the classical probabilistic model and the language modeling approach, and which explicitly combines documents, queries, and relevance in a single formalism; second, a new method for modeling exchangeable sequences of discrete random variables which does not make any structural assumptions about the data and which can also handle rare events. Thus his book is of major interest to researchers and graduate students in information retrieval who specialize in relevance modeling, ranking algorithms, and language modeling.

Download or read book Information Retrieval Systems written by Gerald J. Kowalski and published by Springer. This book was released on 2007-08-23 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: The growth of the Internet and the availability of enormous volumes of data in digital form have necessitated intense interest in techniques to assist the user in locating data of interest. The Internet has over 350 million pages of data and is expected to reach over one billion pages by the year 2000. Buried on the Internet are both valuable nuggets to answer questions as well as a large quantity of information the average person does not care about. The Digital Library effort is also progressing, with the goal of migrating from the traditional book environment to a digital library environment. The challenge to both authors of new publications that will reside on this information domain and developers of systems to locate information is to provide the information and capabilities to sort out the non-relevant items from those desired by the consumer. In effect, as we proceed down this path, it will be the computer that determines what we see versus the human being. The days of going to a library and browsing the new book shelf are being replaced by electronic searching the Internet or the library catalogs. Whatever the search engines return will constrain our knowledge of what information is available. An understanding of Information Retrieval Systems puts this new environment into perspective for both the creator of documents and the consumer trying to locate information.

Download or read book Data Science for Fake News written by Deepak P and published by Springer Nature. This book was released on 2021-04-29 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of fake news detection, both through a variety of tutorial-style survey articles that capture advancements in the field from various facets and in a somewhat unique direction through expert perspectives from various disciplines. The approach is based on the idea that advancing the frontier on data science approaches for fake news is an interdisciplinary effort, and that perspectives from domain experts are crucial to shape the next generation of methods and tools. The fake news challenge cuts across a number of data science subfields such as graph analytics, mining of spatio-temporal data, information retrieval, natural language processing, computer vision and image processing, to name a few. This book will present a number of tutorial-style surveys that summarize a range of recent work in the field. In a unique feature, this book includes perspective notes from experts in disciplines such as linguistics, anthropology, medicine and politics that will help to shape the next generation of data science research in fake news. The main target groups of this book are academic and industrial researchers working in the area of data science, and with interests in devising and applying data science technologies for fake news detection. For young researchers such as PhD students, a review of data science work on fake news is provided, equipping them with enough know-how to start engaging in research within the area. For experienced researchers, the detailed descriptions of approaches will enable them to take seasoned choices in identifying promising directions for future research.