Document Processing Using Machine Learning

Author : Sk Md Obaidullah
Publisher : CRC Press
ISBN 13 : 100073983X
Total Pages : 148 pages
Book Rating : 4.31/5 ( download)

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Download or read book Document Processing Using Machine Learning written by Sk Md Obaidullah and published by CRC Press. This book was released on 2019-11-25 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Simone Marinai
Publisher : Springer Science & Business Media
ISBN 13 : 3540762795
Total Pages : 435 pages
Book Rating : 4.99/5 ( download)

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Download or read book Machine Learning in Document Analysis and Recognition written by Simone Marinai and published by Springer Science & Business Media. This book was released on 2008-01-10 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Stefano Ferilli
Publisher : Springer Science & Business Media
ISBN 13 : 085729198X
Total Pages : 313 pages
Book Rating : 4.81/5 ( download)

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Download or read book Automatic Digital Document Processing and Management written by Stefano Ferilli and published by Springer Science & Business Media. This book was released on 2011-01-03 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Sonali Sahu
Publisher : Packt Publishing Ltd
ISBN 13 : 1803233532
Total Pages : 246 pages
Book Rating : 4.36/5 ( download)

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Download or read book Intelligent Document Processing with AWS AI/ML written by Sonali Sahu and published by Packt Publishing Ltd. This book was released on 2022-10-21 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Robert Munro
Publisher : Simon and Schuster
ISBN 13 : 1617296740
Total Pages : 422 pages
Book Rating : 4.41/5 ( download)

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Download or read book Human-in-the-Loop Machine Learning written by Robert Munro and published by Simon and Schuster. This book was released on 2021-07-20 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Horst Bunke
Publisher : World Scientific
ISBN 13 : 9810220464
Total Pages : 282 pages
Book Rating : 4.64/5 ( download)

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Download or read book Document Image Analysis written by Horst Bunke and published by World Scientific. This book was released on 1994 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : M.A. Jabbar
Publisher : CRC Press
ISBN 13 : 1000794741
Total Pages : 257 pages
Book Rating : 4.48/5 ( download)

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Download or read book Machine Learning Methods for Signal, Image and Speech Processing written by M.A. Jabbar and published by CRC Press. This book was released on 2022-09-01 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: The signal processing (SP) landscape has been enriched by recent advances in artificial intelligence (AI) and machine learning (ML), yielding new tools for signal estimation, classification, prediction, and manipulation. Layered signal representations, nonlinear function approximation and nonlinear signal prediction are now feasible at very large scale in both dimensionality and data size. These are leading to significant performance gains in a variety of long-standing problem domains like speech and Image analysis. As well as providing the ability to construct new classes of nonlinear functions (e.g., fusion, nonlinear filtering). This book will help academics, researchers, developers, graduate and undergraduate students to comprehend complex SP data across a wide range of topical application areas such as social multimedia data collected from social media networks, medical imaging data, data from Covid tests etc. This book focuses on AI utilization in the speech, image, communications and yirtual reality domains.

Author : Alexander Mehler
Publisher : Springer
ISBN 13 : 3642226132
Total Pages : 398 pages
Book Rating : 4.37/5 ( download)

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Download or read book Modeling, Learning, and Processing of Text-Technological Data Structures written by Alexander Mehler and published by Springer. This book was released on 2011-10-14 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Benjamin Bengfort
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491962992
Total Pages : 332 pages
Book Rating : 4.92/5 ( download)

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Download or read book Applied Text Analysis with Python written by Benjamin Bengfort and published by "O'Reilly Media, Inc.". This book was released on 2018-06-11 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations Perform document classification and topic modeling Steer the model selection process with visual diagnostics Extract key phrases, named entities, and graph structures to reason about data in text Build a dialog framework to enable chatbots and language-driven interaction Use Spark to scale processing power and neural networks to scale model complexity

Author : Jeremy Howard
Publisher : O'Reilly Media
ISBN 13 : 1492045497
Total Pages : 624 pages
Book Rating : 4.96/5 ( download)

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Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala