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Intelligent Document Processing (IDP): A Comprehensive Guide to Streamlining Document Management

Intelligent Document Processing (IDP): A Comprehensive Guide to Streamlining Document Management
Author: Rick Spair
Publisher: Rick Spair
Total Pages: 149
Release:
Genre: Computers
ISBN:

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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.


Intelligent Document Processing

Intelligent Document Processing
Author: Lahiru Fernando
Publisher: Notion Press
Total Pages: 256
Release: 2023-08-09
Genre: Computers
ISBN:

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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 with AWS AI/ML

Intelligent Document Processing with AWS AI/ML
Author: Sonali Sahu
Publisher: Packt Publishing Ltd
Total Pages: 246
Release: 2022-10-21
Genre: Computers
ISBN: 1803233532

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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.


An Artificial Intelligence Based Approach to Automate Document Processing in Business Area

An Artificial Intelligence Based Approach to Automate Document Processing in Business Area
Author: Ta Hang Chen
Publisher:
Total Pages: 72
Release: 2021
Genre:
ISBN:

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Automatic document processing is always a strategy for business executives to improve operational efficiency. With Optical Character Recognition (OCR) and machine learning techniques, businesses are able to apply Artificial Intelligence (AI) to automate the process. However, introducing an AI application to business is challenging; it is easy to fail because of the complexity between the technical and organizational components. This thesis considers document processing from a sociotechnical system perspective and leverages a four-step system analysis approach to identify the critical components. This research also proposes a machine learning model using Support Vector Machine (SVM) as the classifier and Word2vec embeddings as document features to classify business documents. The proposed model reaches a 0.872 Macro F1-score using scanned business documents from the RVL-CDIP dataset. The proposed model outperforms the other commonly used rule-based algorithms, RIPPER and PART, showing that the proposed model is potentially suitable to be deployed into business to classify the documents.


Document Processing Using Machine Learning

Document Processing Using Machine Learning
Author: Sk Md Obaidullah
Publisher: CRC Press
Total Pages: 183
Release: 2019-11-25
Genre: Computers
ISBN: 1000739538

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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 Capture with Ephesoft

Intelligent Document Capture with Ephesoft
Author: Pat Myers
Publisher: Packt Publishing Ltd
Total Pages: 164
Release: 2015-08-24
Genre: Computers
ISBN: 1785284932

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Automate the processing of scanned and digital documents by improving accuracy using web-based open and modern intelligent document capture software About This Book Learn how to implement the benefits of intelligent document capture using Ephesoft Enterprise 4 Leverage the power of the open platform to run it as a classic intake capture system to make your current portals or applications more intelligent A practical guide providing examples for optimizing document capture for your business Who This Book Is For This book is intended for information technology professionals interested in installing and configuring Ephesoft Enterprise for their organization, but it is a valuable resource for anyone interested in learning about intelligent document capture. What You Will Learn Discover the benefits of using intelligent document capture in your work place Learn to capture, classify, and separate any type of document Extract important information from your documents Transfer the documents and data into your content management system Customize Ephesoft to meet your unique business requirements Understand the integration techniques using the Ephesoft web services API Convert your paper archive to electronic records efficiently Automate business processes that depend on documents in paper, fax, or email attachment format Implement distributed capture for mailroom automation In Detail Every organization, public or private, processes documents in various formats, especially paper and fax formats. Processing documents manually is an expensive and time-consuming endeavor. Ephesoft Enterprise is a modern document capture solution that allows an organization to automate the business process. It uses powerful technology to classify and capture the vital information from the document's content. This helps to minimize the time your company spends on reviewing and processing any physical and electronic documents. This book teaches you about document capture in general and implementation of document capture using Ephesoft. Start by learning about document capture and how Ephesoft revolutionized the industry. Progress to a tour of key features, including operator and administrator interfaces and then learn to configure Ephesoft to process your business's specific document types and extract content from those documents. You will also get to know the advanced customization techniques that make Ephesoft accommodate your unique business needs. Finally, the book concludes by teaching you how to embed the classification and extraction functionality using Ephesoft's web services. By the end, you will learn to optimize the processing of your documents, saving your company time and money. Style and approach This is a step-by-step guide on how to configure and use Ephesoft using an accounts payable use case. The book will start with basic techniques and progress to more advanced features that allow you to leverage the power for a modern powerful capture system.


Intelligent Speech Signal Processing

Intelligent Speech Signal Processing
Author: Nilanjan Dey
Publisher: Academic Press
Total Pages: 210
Release: 2019-06-15
Genre: Technology & Engineering
ISBN: 0128181303

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Intelligent Speech Signal Processing investigates the utilization of speech analytics across several systems and real-world activities, including sharing data analytics related information, creating collaboration networks between several participants, and implementing video-conferencing in different application areas. It provides a forum for readers to discover the characteristics of intelligent speech signal processing systems across different domains. Chapters focus on the latest applications of speech data analysis and management tools across different recording systems. The book emphasizes the multi-disciplinary nature of the field, presenting different applications and challenges with extensive studies on the design, implementation, development, and management of intelligent systems, neural networks, and related machine learning techniques for speech signal processing. Highlights different data analytics techniques in speech signal processing, including machine learning, and data mining Illustrates different applications and challenges across the design, implementation, and management of intelligent systems and neural networks techniques for speech signal processing Includes coverage of biomodal speech recognition, voice activity detection, spoken language and speech disorder identification, automatic speech to speech summarization, and convolutional neural networks


INTELLIGENT AUTOMATION

INTELLIGENT AUTOMATION
Author: PASCAL. BARKIN BORNET (IAN. WIRTZ, JOCHEN.)
Publisher:
Total Pages: 431
Release: 2020
Genre: Electronic books
ISBN: 9811235848

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Automatic Digital Document Processing and Management

Automatic Digital Document Processing and Management
Author: Stefano Ferilli
Publisher: Springer Science & Business Media
Total Pages: 313
Release: 2011-01-03
Genre: Computers
ISBN: 085729198X

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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.


Artificial Intelligence in Asset Management

Artificial Intelligence in Asset Management
Author: Söhnke M. Bartram
Publisher: CFA Institute Research Foundation
Total Pages: 95
Release: 2020-08-28
Genre: Business & Economics
ISBN: 195292703X

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Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.