Machine Learning in Document Analysis and Recognition

Machine Learning in Document Analysis an

📁 Machine Learning in Document Analysis and Recognition

✍ Simone Marinai (auth.), Prof. Simone Marinai, Prof. Hiromichi Fujisawa (eds.) 📂 Library 📅 2008 🏛 Springer-Verlag Berlin Heidelberg 🌐 English

<p>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

Machine Learning in Document Analysis an

📁 Machine Learning in Document Analysis and Recognition

✍ Simone Marinai (auth.), Prof. Simone Marinai, Prof. Hiromichi Fujisawa (eds.) 📂 Library 📅 2008 🏛 Springer-Verlag Berlin Heidelberg 🌐 English

<p>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

Sequential Methods in Pattern Recognitio

📁 Sequential Methods in Pattern Recognition and Machine Learning

✍ K.S. Fu (Eds.) 📂 Library 📅 1968 🏛 Academic Press 🌐 English

Sequential Methods in Pattern Recognitio

📁 Sequential Methods in Pattern Recognition and Machine Learning

✍ K.S. Fu (Eds.) 📂 Library 📅 1968 🏛 Academic Press 🌐 English

Pattern Recognition and Machine Learning

📁 Pattern Recognition and Machine Learning

✍ Christopher M. Bishop 📂 Library 📅 2007 🏛 Springer 🌐 English

<P>This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no o

Pattern Recognition and Machine Learning

📁 Pattern Recognition and Machine Learning

✍ Christopher Bishop 📂 Library 📅 2007 🏛 Springer 🌐 English

内容简介 · · · · · · The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while