Document Image Processing

The Handbook of Document Image Processing and Recognition is a comprehensive resource on the latest methods and techniques in document image processing and recognition. Each chapter provides a clear overview of the topic followed by the state of the art of techniques used – including elements of comparison between them – along with supporting references to archival publications, for those interested in delving deeper into topics addressed. Rather than favor a particular approach, the text enables the reader to make an informed decision for their specific problems.

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

This book continues first one of the same authors “Adaptive Image Processing Algorithms for Printing” and presents methods and software solutions for copying and scanning various types of documents by conventional office equipment, offering techniques for correction of distortions and enhancement of scanned documents; techniques for automatic cropping and de-skew; approaches for segmentation of text and picture regions; documents classifiers; approach for vectorization of symbols by approximation of their contour by curves; methods for optimal compression of scanned documents, algorithm for stitching parts of large originals; copy-protection methods by microprinting and embedding of hidden information to hardcopy; algorithmic approach for toner saving. In addition, method for integral printing is considered. Described techniques operate in automatic mode thanks to machine learning or ingenious heuristics. Most the techniques presented have a low computational complexity and memory consumption due to they were designed for firmware of embedded systems or software drivers. The book reflects the authors’ practical experience in algorithm development for industrial R&D.

Optical character recognition and document image analysis have become very important areas with a fast growing number of researchers in the field. This comprehensive handbook with contributions by eminent experts, presents both the theoretical and practical aspects at an introductory level wherever possible.

The book focuses on one of the key issues in document image processing – graphical symbol recognition, which is a sub-field of the larger research domain of pattern recognition. It covers several approaches: statistical, structural and syntactic, and discusses their merits and demerits considering the context. Through comprehensive experiments, it also explores whether these approaches can be combined. The book presents research problems, state-of-the-art methods that convey basic steps as well as prominent techniques, evaluation metrics and protocols, and research standpoints/directions that are associated with it. However, it is not limited to straightforward isolated graphics (visual patterns) recognition; it also addresses complex and composite graphical symbols recognition, which is motivated by real-world industrial problems.

Document Processing Using Machine Learning aims at presenting a handful of resources for students and researchers working in the document image analysis (DIA) domain using machine learning since it covers multiple document processing problems. Starting with an explanation of how Artificial Intelligence (AI) plays an important role in this domain, the book further discusses how different machine learning algorithms can be applied for classification/recognition and clustering problems regardless the type of input data: images or text. In brief, the book offers comprehensive coverage of the most essential topics, including: · The role of AI for document image analysis · Optical character recognition · Machine learning algorithms for document analysis · Extreme learning machines and their applications · Mathematical foundation for Web text document analysis · Social media data analysis · Modalities for document dataset generation This book serves both undergraduate and graduate scholars in Computer Science/Information Technology/Electrical and Computer Engineering. Further, it is a great fit for early career research scientists and industrialists in the domain.

Interest in the automatic processing and analysis of document images has been rapidly increasing during the past few years. This book addresses the different subfields of document image analysis, including preprocessing and segmentation, form processing, handwriting recognition, line drawing and map processing, and contextual processing.