The book constitutes the refereed proceedings of the Summit on Arabic and Chinese Handwriting Recognition, SACH 2006, held in College Park, USA, September 27-28, 2006. The 16 revised full papers presented were carefully reviewed and selected from a total of over 60 submissions. The first six papers deal directly with Arabic handwriting together with a short historic survey of the language and techniques used in recognition. Five papers present the current research in Chinese handwriting and three more papers deal with cross cutting methods applied to other languages. The book closes with two articles on recognition of English and south Indian handwriting.
Since their first inception, automatic reading systems have evolved substantially, yet the recognition of handwriting remains an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic handwriting recognition. However, no standard procedures for building Markov model-based recognizers have yet been established. This text provides a comprehensive overview of the application of Markov models in the field of handwriting recognition, covering both hidden Markov models and Markov-chain or n-gram models. First, the text introduces the typical architecture of a Markov model-based handwriting recognition system, and familiarizes the reader with the essential theoretical concepts behind Markovian models. Then, the text reviews proposed solutions in the literature for open problems in applying Markov model-based approaches to automatic handwriting recognition.
Designing machines that can read handwriting like human beings has been an ambitious goal for more than half a century, driving talented researchers to explore diverse approaches. Obstacles have often been encountered that at first appeared insurmountable but were indeed overcome before long. Yet some open issues remain to be solved. As an indispensable branch, Chinese handwriting recognition has been termed as one of the most difficult Pattern Recognition tasks. Chinese handwriting recognition poses its own unique challenges, such as huge variations in strokes, diversity of writing styles, and a large set of confusable categories. With ever-increasing training data, researchers have pursued elaborate algorithms to discern characters from different categories and compensate for the sample variations within the same category. As a result, Chinese handwriting recognition has evolved substantially and amazing achievements can be seen. This book introduces integral algorithms used in Chinese handwriting recognition and the applications of Chinese handwriting recogniers. The first part of the book covers both widespread canonical algorithms to a reliable recognizer and newly developed scalable methods in Chinese handwriting recognition. The recognition of Chinese handwritten text is presented systematically, including instructive guidelines for collecting samples, novel recognition paradigms, distributed discriminative learning of appearance models and distributed estimation of contextual models for large categories, in addition to celebrated methods, e.g. Gradient features, MQDF and HMMs. In the second part of this book, endeavors are made to create a friendlier human-machine interface through application of Chinese handwriting recognition. Four scenarios are exemplified: grid-assisted input, shortest moving input, handwritten micro-blog, and instant handwriting messenger. All the while, the book moves from basic to more complex approaches, also providing a list for further reading with literature comments.
This Guide to OCR for Arabic Scripts is the first book of its kind, specifically devoted to this emerging field. Topics and features: contains contributions from the leading researchers in the field; with a Foreword by Professor Bente Maegaard of the University of Copenhagen; presents a detailed overview of Arabic character recognition technology, covering a range of different aspects of pre-processing and feature extraction; reviews a broad selection of varying approaches, including HMM-based methods and a recognition system based on multidimensional recurrent neural networks; examines the evaluation of Arabic script recognition systems, discussing data collection and annotation, benchmarking strategies, and handwriting recognition competitions; describes numerous applications of Arabic script recognition technology, from historical Arabic manuscripts to online Arabic recognition.
The book is a collection of invited chapters by experts in Chinese document and text processing, and is part of a series on Language Processing, Pattern Recognition, and Intelligent Systems. The chapters introduce the latest advances and state-of-the-art methods for Chinese document image analysis and recognition, font design, text analysis and speaker recognition. Handwritten Chinese character recognition and text line recognition are at the core of document image analysis (DIA), and therefore, are addressed in four chapters for different scripts (online characters, offline characters, ancient characters, and text lines). Two chapters on character recognition pay much attention to deep convolutional neural networks (CNNs), which are widely used and performing superiorly in various pattern recognition problems. A chapter is contributed to describe a large handwriting database consisting both online and offline characters and text pages. Postal mail reading and writer identification, addressed in two chapters, are important applications of DIA. The collection can serve as reference for students and engineers in Chinese document and text processing and their applications.
For decades, optimization methods such as Fuzzy Logic, Artificial Neural Networks, Firefly, Simulated annealing, and Tabu search, have been capable of handling and tackling a wide range of real-world application problems in society and nature. Analysts have turned to these problem-solving techniques in the event during natural disasters and chaotic systems research. The Handbook of Research on Artificial Intelligence Techniques and Algorithms highlights the cutting edge developments in this promising research area. This premier reference work applies Meta-heuristics Optimization (MO) Techniques to real world problems in a variety of fields including business, logistics, computer science, engineering, and government. This work is particularly relevant to researchers, scientists, decision-makers, managers, and practitioners.
The fields of computer vision and image processing are constantly evolving as new research and applications in these areas emerge. Staying abreast of the most up-to-date developments in this field is necessary in order to promote further research and apply these developments in real-world settings. Computer Vision: Concepts, Methodologies, Tools, and Applications is an innovative reference source for the latest academic material on development of computers for gaining understanding about videos and digital images. Highlighting a range of topics, such as computational models, machine learning, and image processing, this multi-volume book is ideally designed for academicians, technology professionals, students, and researchers interested in uncovering the latest innovations in the field.
The fifth volume in this book series consists of a collection of new papers written by a diverse group of international scholars. Papers and presentations were carefully selected from 160 papers submitted to the International Conference on Pattern Recognition and Artificial Intelligence held in Montreal, Quebec (May 2018) and an associated free public lecture entitled 'Artificial Intelligence and Pattern Recognition: Trendy Technologies in Our Modern Digital World'. Chapters address topics such as the evolution of AI, natural language processing, off and on-line handwriting analysis, tracking and detection systems, neural networks, rating video games, computer-aided diagnosis, and digital learning.Within an increasingly digital world, 'artificial intelligence' is becoming a household term and a topic of great interest to many people worldwide. Pattern recognition, in using key features to classify data, has a strong relationship with artificial intelligence. This book not only complements other monographs in the series, it also provides the latest information. It is geared to promote interest and understanding about pattern recognition and artificial intelligence to the general public. It may also be of interest to graduate students and researchers in the field. Rather than focusing on one specific area, the book introduces readers to various basic concepts and to various potential areas where pattern recognition and artificial intelligence can be applied to make valuable contributions to other fields such as medicine, teaching and learning, forensic science, surveillance, online reviews, computer vision and object tracking.
This volume constitutes the refereed proceedings of the 4th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2021, held in Msida, Malta, in December 2021. Due to the COVID-19 pandemic the conference was held online. The 19 full papers and 14 short papers presented were carefully reviewed and selected from 84 submissions. The papers are organized in the following topical sections: healthcare: medical imaging and informatics; computer vision and pattern recognition; document analysis and recognition; signal processing and machine learning; satellite imaging and remote sensing.
This book constitutes the proceedings of the international workshops co-located with the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland, in September 2021.The total of 59 full and 12 short papers presented in this book were carefully selected from 96 contributions and divided into two volumes. Part I contains 29 full and 4 short papers that stem from the following meetings: ICDAR 2021 Workshop on Graphics Recognition (GREC); ICDAR 2021 Workshop on Camera-Based Document Analysis and Recognition (CBDAR); ICDAR 2021 Workshop on Arabic and Derived Script Analysis and Recognition (ASAR 2021); ICDAR 2021 Workshop on Computational Document Forensics (IWCDF). The main topics of the contributions are document processing; physical and logical layout analysis; text and symbol recognition; handwriting recognition; signature verification and document forensics, and others. “Accurate Graphic Symbol Detection in Ancient Document Digital Reproductions” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
