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This text reviews the issues involved in handling and processing digital documents. Examining the full range of a document’s lifetime, the book covers acquisition, representation, security, pre-processing, layout analysis, understanding, analysis of single components, information extraction, filing, indexing and retrieval. Features: provides a list of acronyms and a glossary of technical terms; contains appendices covering key concepts in machine learning, and providing a case study on building an intelligent system for digital document and library management; discusses issues of security, and legal aspects of digital documents; examines core issues of document image analysis, and image processing techniques of particular relevance to digitized documents; reviews the resources available for natural language processing, in addition to techniques of linguistic analysis for content handling; investigates methods for extracting and retrieving data/information from a document.
The world of document management is evolving rapidly, and organizations are increasingly turning to Intelligent Document Processing (IDP) to streamline their document management processes. This comprehensive guide serves as a valuable resource for individuals and organizations embarking on their IDP journey. It offers a step-by-step approach, practical tips, and best practices to help readers successfully implement IDP and achieve significant improvements in efficiency, accuracy, and cost savings. In today's digital age, the volume and complexity of documents continue to grow exponentially, posing significant challenges for organizations across industries. Traditional manual document management processes are time-consuming, error-prone, and resource-intensive, leading to inefficiencies and missed opportunities. However, the advent of Intelligent Document Processing (IDP) presents a game-changing solution. Intelligent Document Processing combines the power of artificial intelligence, machine learning, and automation technologies to extract and process data from unstructured documents swiftly and accurately. By automating manual tasks, organizations can enhance productivity, improve data accuracy, and optimize their document management workflows. This guide serves as a roadmap for readers looking to harness the potential of IDP and transform their document management practices. The chapters of this guide take readers on a comprehensive journey through the world of IDP. It begins with an introduction to document management and the concept of Intelligent Document Processing. Readers will gain a clear understanding of the benefits and importance of implementing IDP in their organizations. The guide then delves into the key aspects of implementing IDP. It covers topics such as assessing document management needs, identifying document types and formats, analyzing document volume and complexity, and evaluating existing document management processes. These chapters provide practical insights, tips, and strategies to help readers assess their current state and identify areas for improvement. As the journey progresses, the guide dives into creating an IDP strategy, including setting clear goals and objectives, selecting the right IDP solution, and defining key performance indicators (KPIs). It emphasizes the importance of customization and adaptation to align with specific organizational needs and goals. The guide further explores preparing documents for IDP, including standardizing formats and layouts, optimizing image quality and resolution, and implementing document classification and indexing. It provides detailed guidance on leveraging intelligent capture technologies, extracting data from structured and unstructured documents, and validating and verifying extracted data. The chapters also cover crucial aspects such as integrating IDP with existing systems, monitoring and measuring IDP performance, change management, and user adoption. They address data security and compliance requirements, as well as provide real-world case studies and success stories to inspire and educate readers. Throughout the guide, readers will find tips, recommendations, and best practices from industry leaders who have successfully implemented IDP. These insights serve as valuable lessons learned and provide practical guidance for readers as they embark on their IDP journey. In conclusion, this comprehensive guide equips readers with the knowledge and tools needed to implement Intelligent Document Processing successfully. By following the chapters, tips, recommendations, and strategies outlined in this guide, organizations can streamline their document management processes, achieve significant improvements in efficiency and accuracy, and drive tangible business outcomes. The IDP journey begins here, offering endless possibilities for optimizing document management in the digital era.
Integrating Business Management Processes: Volume 2: Support and Assurance Processes (978-0-367-48548-1) Shelving Guide: Business & Management The backbone of any organisation is its management system. It must reflect the needs of the organisation and the requirements of its customers. Compliance with legal requirements and ethical environmental practices contributes towards the sustainability of the management system. Whatever the state of maturity of the management, this book, one of three, provides useful guidance to design, implement, maintain and improve its effectiveness. This volume provides a comprehensive coverage of the key support and assurance processes. Topics include document control, communication, marketing, information systems and technology, human resource management, training and development, customer relations management, financial management and measurement and analysis to name a few. This book, with its series of examples and procedures, shows how organisations can benefit from satisfying customer requirement and the requirements of ISO standards to gain entry into lucrative markets. Titus De Silva is a consultant in management skills development, pharmacy practice, quality management and food safety and an advisor to the newly established National Medicines Regulatory Authority (NMRA) in Sri Lanka.
Competitive strategies and higher education-industry collaboration policies are playing an important role in fostering the reputation and international rankings of higher education institutions. The positive impact of these policies may best be observed in economic and social outputs of many countries such as the USA, Singapore, South Korea, EU countries, and Turkey. However, the number of academic publications that specifically concentrate on the impact of these policies on higher education institutions and authorities remains relatively limited. Digital Transformation and Internationalization Strategies in Organizations covers a wide range of issues and topics, including employment systems, quality management systems, international ranking systems in higher education, education and language policies in higher education, and business models employed in techno-parks. This book helps higher education institutions manage their manpower and become cognizant of the factors that may exert a drastic impact on their success. It is ideal for managers, executives, IT consultants, researchers, practitioners, academics, professors, and undergraduate and postgraduate students.
This book brings all the major and frontier topics in the field of document analysis together into a single volume, creating a unique reference source that will be invaluable to a large audience of researchers, lecturers and students working in this field. With chapters written by some of the most distinguished researchers active in this field, this book addresses recent advances in digital document processing research and development.
This volume Future Control and Automation- Volume 2 includes best papers from 2012 2nd International Conference on Future Control and Automation (ICFCA 2012) held on July 1-2, 2012, Changsha, China. Future control and automation is the use of control systems and information technologies to reduce the need for human work in the production of goods and services. This volume can be divided into six sessions on the basis of the classification of manuscripts considered, which is listed as follows: Mathematical Modeling, Analysis and Computation, Control Engineering, Reliable Networks Design, Vehicular Communications and Networking, Automation and Mechatronics.
This book constitutes the proceedings of the BPM Forum held at the 21st International Conference on Business Process Management, BPM 2023, which took place in Utrecht, The Netherlands, in September 2023. The Blockchain Forum provided a platform for exploring and discussing innovative ideas on the intersection of BPM and blockchain technology. The RPA Forum focused on the use of the Robotic Process Automation (RPA) in the field of Business Process Management. The Educators Forum brought together educators within the BPM community for sharing resources to improve the practice of teaching BPM-related topics. The 18 full papers included in this volume were carefully reviewed and selected from a total of 39 submissions.
Online communities generate massive volumes of natural language data and the social sciences continue to learn how to best make use of this new information and the technology available for analyzing it. Text Mining brings together a broad range of contemporary qualitative and quantitative methods to provide strategic and practical guidance on analyzing large text collections. This accessible book, written by a sociologist and a computer scientist, surveys the fast-changing landscape of data sources, programming languages, software packages, and methods of analysis available today. Suitable for novice and experienced researchers alike, the book will help readers use text mining techniques more efficiently and productively.
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.