machine learning document analysis (30 risultati)

Condizione: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.

Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Brand new book. Fast ship. Please provide full street address as we are not able to ship toPOboxaddress.

Condizione: New. pp. 448.

Condizione: New. pp. 448 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

Condizione: New. pp. 448.

Condizione: New.

Condizione: New.

Condizione: New. In.

Condizione: New. In.

Condizione: New.

Condizione: New. pp. 448.

Buch. Condizione: Neu. Neuware -The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphicalcomponents of a document and to extract information. With rst papers dating back to the 1960¿s, DAR is a mature but still gr- ing research eld with consolidated and known techniques. Optical Character Recognition (OCR) engines are some of the most widely recognized pr- ucts of the research in this eld, while broader DAR techniques are nowadays studied and applied to other industrial and o ce automation systems. In the machine learning community, one of the most widely known - search problems addressed in DAR is recognition of unconstrained handwr- ten characters which has been frequently used in the past as a benchmark for evaluating machine learning algorithms, especially supervised classi ers. However, developing a DAR system is a complex engineering task that involves the integration of multiple techniques into an organic framework. A reader may feel that the use of machine learning algorithms is not approp- ate for other DAR tasks than character recognition. On the contrary, such algorithms have been massively used for nearly all the tasks in DAR. With large emphasis being devoted to character recognition and word recognition, other tasks such as pre-processing, layout analysis, character segmentation, and signature veri cation have also bene ted much from machine learning algorithms.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 448 pp. Englisch.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphicalcomponents of a document and to extract information. With rst papers dating back to the 1960's, DAR is a mature but still gr- ing research eld with consolidated and known techniques. Optical Character Recognition (OCR) engines are some of the most widely recognized pr- ucts of the research in this eld, while broader DAR techniques are nowadays studied and applied to other industrial and o ce automation systems. In the machine learning community, one of the most widely known - search problems addressed in DAR is recognition of unconstrained handwr- ten characters which has been frequently used in the past as a benchmark for evaluating machine learning algorithms, especially supervised classi ers. However, developing a DAR system is a complex engineering task that involves the integration of multiple techniques into an organic framework. A reader may feel that the use of machine learning algorithms is not approp- ate for other DAR tasks than character recognition. On the contrary, such algorithms have been massively used for nearly all the tasks in DAR. With large emphasis being devoted to character recognition and word recognition, other tasks such as pre-processing, layout analysis, character segmentation, and signature veri cation have also bene ted much from machine learning algorithms.

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphicalcomponents of a document and to extract information. With rst papers dating back to the 1960's, DAR is a mature but still gr- ing research eld with consolidated and known techniques. Optical Character Recognition (OCR) engines are some of the most widely recognized pr- ucts of the research in this eld, while broader DAR techniques are nowadays studied and applied to other industrial and o ce automation systems. In the machine learning community, one of the most widely known - search problems addressed in DAR is recognition of unconstrained handwr- ten characters which has been frequently used in the past as a benchmark for evaluating machine learning algorithms, especially supervised classi ers. However, developing a DAR system is a complex engineering task that involves the integration of multiple techniques into an organic framework. A reader may feel that the use of machine learning algorithms is not approp- ate for other DAR tasks than character recognition. On the contrary, such algorithms have been massively used for nearly all the tasks in DAR. With large emphasis being devoted to character recognition and word recognition, other tasks such as pre-processing, layout analysis, character segmentation, and signature veri cation have also bene ted much from machine learning algorithms.

Paperback. Condizione: Brand New. 434 pages. 9.25x6.10x1.01 inches. In Stock.

Hardcover. Condizione: Brand New. 1st edition. 433 pages. 6.50x9.50x1.00 inches. In Stock.

Hardcover. Condizione: Like New. Like New. book.

Paperback. Condizione: Like New. Like New. book.

Softcover. Condizione: New. Condizione sovraccoperta: no dj. First. The integration of machine learning and legal document analysis has the potential to revolutionize the way legal professionals review and analyze documents. 'Machine Learning for Legal Document Analysis' explores the possibilities and challenges of machine learning in legal document analysis, examining its potential applications, benefits, and limitations. By discussing the various machine learning tools and techniques available for legal document analysis, this book provides a comprehensive guide for legal professionals seeking to leverage machine learning in their practice. It also addresses the key challenges and considerations associated with machine learning-assisted legal document analysis, including data quality, bias, and explainability. As machine learning continues to transform the legal profession, this book offers practical insights and guidance for legal professionals seeking to harness its potential. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Condizione: New. Condizione sovraccoperta: no dj. First. In today's digital age, the volume of legal documents is staggering, and the need for efficient analysis has never been greater. This book provides a comprehensive guide to developing machine learning models for complex legal document analysis, covering the latest techniques and tools for text classification, sentiment analysis, and named entity recognition. By harnessing the power of machine learning, legal professionals can automate the process of document review, reduce costs, and improve accuracy. With a focus on real-world applications, this book explores the challenges and opportunities of machine learning in legal document analysis, from data preprocessing to model evaluation. By understanding the strengths and weaknesses of machine learning models, legal professionals can make informed decisions about their use in document analysis. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Condizione: New. Condizione sovraccoperta: no dj. First. Machine learning is a powerful tool for analyzing complex documents, and this book provides a comprehensive guide to its applications in document analysis. With its clear explanations, step-by-step examples, and expert insights, readers will gain a deep understanding of the science behind machine learning and how to apply it to their document analysis skills. From the basics of machine learning theory to advanced techniques for analyzing complex documents, this book covers it all. Whether you're a data scientist, a researcher, or simply a document analyst, this book is an essential resource for anyone looking to elevate their skills and stay ahead of the competition. By mastering the art of machine learning, readers will be able to analyze complex documents with ease and make informed decisions. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Condizione: New. Condizione sovraccoperta: no dj. First. Streamlining Contract Review with AI-Powered Document Analysis and Machine Learning provides a comprehensive guide to leveraging artificial intelligence and machine learning to optimize contract review processes. By harnessing the power of AI and machine learning, organizations can significantly reduce review times, improve accuracy, and enhance collaboration. This book explores the latest techniques and tools for automating contract review, from natural language processing to machine learning algorithms. With its focus on practical applications and real-world examples, Streamlining Contract Review with AI-Powered Document Analysis and Machine Learning is an essential resource for contract managers, lawyers, and business leaders seeking to revolutionize their contract review workflows. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Condizione: New. Condizione sovraccoperta: no dj. First. Streamlining Contract Review with Document Analysis and Machine Learning is a comprehensive guide that shows businesses how to streamline contract review using document analysis and machine learning. By leveraging the power of machine learning, this book provides a practical approach to identifying and mitigating contract risks, improving collaboration, and reducing the risk of disputes. With a focus on real-world examples and case studies, this book covers the latest advancements in AI-powered contract review, from natural language processing to data analytics. By following the techniques and strategies outlined in this book, businesses can improve their contract review and reduce the risk of disputes. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 448 pp. Englisch.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 448 pp. Englisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphicalcomponents of a document and to extract information. With rst papers dating back to the 1960¿s, DAR is a mature but still gr- ing research eld with consolidated and known techniques. Optical Character Recognition (OCR) engines are some of the most widely recognized pr- ucts of the research in this eld, while broader DAR techniques are nowadays studied and applied to other industrial and o ce automation systems. In the machine learning community, one of the most widely known - search problems addressed in DAR is recognition of unconstrained handwr- ten characters which has been frequently used in the past as a benchmark for evaluating machine learning algorithms, especially supervised classi ers. However, developing a DAR system is a complex engineering task that involves the integration of multiple techniques into an organic framework. A reader may feel that the use of machine learning algorithms is not approp- ate for other DAR tasks than character recognition. On the contrary, such algorithms have been massively used for nearly all the tasks in DAR. With large emphasis being devoted to character recognition and word recognition, other tasks such as pre-processing, layout analysis, character segmentation, and signature veri cation have also bene ted much from machine learning algorithms.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 448 pp. Englisch.

Condizione: New. Print on Demand pp. 448 142 Illus.

Condizione: New. PRINT ON DEMAND pp. 448.