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Document Processing Using Machine Learning by Sk Md Obaidullah,KC Santosh,Teresa Goncalves,Nibaran Das,Kaushik Roy Pdf
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.
Machine Learning in Document Analysis and Recognition by Simone Marinai Pdf
The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphical components of a document and to extract information. This book is a collection of research papers and state-of-the-art reviews by leading researchers all over the world. It includes pointers to challenges and opportunities for future research directions. The main goal of the book is to identify good practices for the use of learning strategies in DAR.
Automatic Digital Document Processing and Management by Stefano Ferilli Pdf
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.
Document Processing Using Machine Learning by Sk Md Obaidullah,KC Santosh,Teresa Goncalves,Nibaran Das,Kaushik Roy Pdf
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.
Intelligent Document Processing with AWS AI/ML by Sonali Sahu Pdf
Build real-world artificial intelligence applications across industries with the help of intelligent document processing Key FeaturesTackle common document processing problems to extract value from any type of documentUnlock deeper levels of insights on IDP in a more structured and accelerated way using AWS AI/MLApply your knowledge to solve real document analysis problems in various industry applicationsBook Description With the volume of data growing exponentially in this digital era, it has become paramount for professionals to process this data in an accelerated and cost-effective manner to get value out of it. Data that organizations receive is usually in raw document format, and being able to process these documents is critical to meeting growing business needs. This book is a comprehensive guide to helping you get to grips with AI/ML fundamentals and their application in document processing use cases. You'll begin by understanding the challenges faced in legacy document processing and discover how you can build end-to-end document processing pipelines with AWS AI services. As you advance, you'll get hands-on experience with popular Python libraries to process and extract insights from documents. This book starts with the basics, taking you through real industry use cases for document processing to deliver value-based care in the healthcare industry and accelerate loan application processing in the financial industry. Throughout the chapters, you'll find out how to apply your skillset to solve practical problems. By the end of this AWS book, you'll have mastered the fundamentals of document processing with machine learning through practical implementation. What you will learnUnderstand the requirements and challenges in deriving insights from a documentExplore common stages in the intelligent document processing pipelineDiscover how AWS AI/ML can successfully automate IDP pipelinesFind out how to write clean and elegant Python code by leveraging AIGet to grips with the concepts and functionalities of AWS AI servicesExplore IDP across industries such as insurance, healthcare, finance, and the public sectorDetermine how to apply business rules in IDPBuild, train, and deploy models with serverless architecture for IDPWho this book is for This book is for technical professionals and thought leaders who want to understand and solve business problems by leveraging insights from their documents. If you want to learn about machine learning and artificial intelligence, and work with real-world use cases such as document processing with technology, this book is for you. To make the most of this book, you should have basic knowledge of AI/ML and python programming concepts. This book is also especially useful for developers looking to explore AI/ML with industry use cases.
Document Image Analysis by Horst Bunke,Patrick Shen-pei Wang,Henry S. Baird Pdf
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.
Intelligent Document Processing by Lahiru Fernando Pdf
Document processing is a topic that has gained much traction for many years due to its complexity and manual effort. Many document management systems got introduced to simplify document management. At the same time, Robotic Process Automation (RPA) evolved at a rapid pace connecting with state-of-the-art technologies such as Machine Learning (ML), Artificial Intelligence (AI), and Natural Language Processing (NLP) to understand the ways humans communicate. The technology used for AI, ML, and NLP enabled the world to build models that can learn by themselves and use their intelligence to understand the content of any given document. Today, Intelligent Document Processing (IDP) and RPA work together to automate most document-related activities, freeing up users to focus on more critical tasks. Intelligent Document Processing: A Guide for Building RPA Solutions is a mini-guide that gives the readers insights on methods to achieve the best out of Intelligent Document Understanding solutions built within RPA workflows. Further, the mini-book provides real-world use cases, technical challenges, best practices, industry trends, links to many external research articles, and detailed discussions focussing on building effective and scalable RPA solutions to process documents intelligently. The book also contains the author's personal experiences on multiple intelligent document automation projects. This mini-book should be seen as an overview of the current state of technology, with practical guidance and solutions. Best used as a reference guide to help you with your “Optical AI” initiatives.
Intelligent Document Processing (IDP): A Comprehensive Guide to Streamlining Document Management by Rick Spair Pdf
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.
Modeling, Learning, and Processing of Text-Technological Data Structures by Alexander Mehler,Kai-Uwe Kühnberger,Henning Lobin,Harald Lüngen,Angelika Storrer,Andreas Witt Pdf
Researchers in many disciplines have been concerned with modeling textual data in order to account for texts as the primary information unit of written communication. The book “Modelling, Learning and Processing of Text-Technological Data Structures” deals with this challenging information unit. It focuses on theoretical foundations of representing natural language texts as well as on concrete operations of automatic text processing. Following this integrated approach, the present volume includes contributions to a wide range of topics in the context of processing of textual data. This relates to the learning of ontologies from natural language texts, the annotation and automatic parsing of texts as well as the detection and tracking of topics in texts and hypertexts. In this way, the book brings together a wide range of approaches to procedural aspects of text technology as an emerging scientific discipline.
Human-in-the-Loop Machine Learning by Robert Munro,Robert Monarch Pdf
Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. Human-in-the-loop machine learning lays out methods for humans and machines to work together effectively. You'll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You'll learn to dreate training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.
Intelligent Algorithms in Software Engineering by Radek Silhavy Pdf
This book gathers the refereed proceedings of the Intelligent Algorithms in Software Engineering Section of the 9th Computer Science On-line Conference 2020 (CSOC 2020), held on-line in April 2020. Software engineering research and its applications to intelligent algorithms have now assumed an essential role in computer science research. In this book, modern research methods, together with applications of machine and statistical learning in software engineering research, are presented.
AWS Certified Machine Learning Study Guide by Shreyas Subramanian,Stefan Natu Pdf
Succeed on the AWS Machine Learning exam or in your next job as a machine learning specialist on the AWS Cloud platform with this hands-on guide As the most popular cloud service in the world today, Amazon Web Services offers a wide range of opportunities for those interested in the development and deployment of artificial intelligence and machine learning business solutions. The AWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam delivers hyper-focused, authoritative instruction for anyone considering the pursuit of the prestigious Amazon Web Services Machine Learning certification or a new career as a machine learning specialist working within the AWS architecture. From exam to interview to your first day on the job, this study guide provides the domain-by-domain specific knowledge you need to build, train, tune, and deploy machine learning models with the AWS Cloud. And with the practice exams and assessments, electronic flashcards, and supplementary online resources that accompany this Study Guide, you’ll be prepared for success in every subject area covered by the exam. You’ll also find: An intuitive and organized layout perfect for anyone taking the exam for the first time or seasoned professionals seeking a refresher on machine learning on the AWS Cloud Authoritative instruction on a widely recognized certification that unlocks countless career opportunities in machine learning and data science Access to the Sybex online learning resources and test bank, with chapter review questions, a full-length practice exam, hundreds of electronic flashcards, and a glossary of key terms AWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam is an indispensable guide for anyone seeking to prepare themselves for success on the AWS Certified Machine Learning Specialty exam or for a job interview in the field of machine learning, or who wishes to improve their skills in the field as they pursue a career in AWS machine learning.
Natural Language Processing with AWS AI Services by Mona M,Premkumar Rangarajan,Julien Simon Pdf
Work through interesting real-life business use cases to uncover valuable insights from unstructured text using AWS AI services Key FeaturesGet to grips with AWS AI services for NLP and find out how to use them to gain strategic insightsRun Python code to use Amazon Textract and Amazon Comprehend to accelerate business outcomesUnderstand how you can integrate human-in-the-loop for custom NLP use cases with Amazon A2IBook Description Natural language processing (NLP) uses machine learning to extract information from unstructured data. This book will help you to move quickly from business questions to high-performance models in production. To start with, you'll understand the importance of NLP in today's business applications and learn the features of Amazon Comprehend and Amazon Textract to build NLP models using Python and Jupyter Notebooks. The book then shows you how to integrate AI in applications for accelerating business outcomes with just a few lines of code. Throughout the book, you'll cover use cases such as smart text search, setting up compliance and controls when processing confidential documents, real-time text analytics, and much more to understand various NLP scenarios. You'll deploy and monitor scalable NLP models in production for real-time and batch requirements. As you advance, you'll explore strategies for including humans in the loop for different purposes in a document processing workflow. Moreover, you'll learn best practices for auto-scaling your NLP inference for enterprise traffic. Whether you're new to ML or an experienced practitioner, by the end of this NLP book, you'll have the confidence to use AWS AI services to build powerful NLP applications. What you will learnAutomate various NLP workflows on AWS to accelerate business outcomesUse Amazon Textract for text, tables, and handwriting recognition from images and PDF filesGain insights from unstructured text in the form of sentiment analysis, topic modeling, and more using Amazon ComprehendSet up end-to-end document processing pipelines to understand the role of humans in the loopDevelop NLP-based intelligent search solutions with just a few lines of codeCreate both real-time and batch document processing pipelines using PythonWho this book is for If you're an NLP developer or data scientist looking to get started with AWS AI services to implement various NLP scenarios quickly, this book is for you. It will show you how easy it is to integrate AI in applications with just a few lines of code. A basic understanding of machine learning (ML) concepts is necessary to understand the concepts covered. Experience with Jupyter notebooks and Python will be helpful.
Web Document Analysis by Apostolos Antonacopoulos,Jianying Hu Pdf
This book provides the first comprehensive look at the emerging field of web document analysis. It sets the scene in this new field by combining state-of-the-art reviews of challenges and opportunities with research papers by leading researchers. Readers will find in-depth discussions on the many diverse and interdisciplinary areas within the field, including web image processing, applications of machine learning and graph theories for content extraction and web mining, adaptive web content delivery, multimedia document modeling and human interactive proofs for web security. Contents: Content Extraction and Web Mining; Document Analysis for Adaptive Content Delivery; Table Understanding on the Web; Web Image Analysis and Retrieval; New Opportunities. Readership: Graduate students and researchers in document-analysis and web communities.
Document Analysis and Recognition – ICDAR 2021 Workshops by Elisa H. Barney Smith,Umapada Pal Pdf
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.