Titlebook: Machine Learning in Document Analysis and Recognition; Simone Marinai,Hiromichi Fujisawa Book 2008 Springer-Verlag Berlin Heidelberg 2008

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書目名稱Machine Learning in Document Analysis and Recognition影響因子(影響力)




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DEMUR

Book 2008th ?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

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adequate-intake

Classification and Learning Methods for Character Recognition: Advances and Remaining Problems,pplied to character recognition, with a special section devoted to the classification of large category set. We then discuss the characteristics of these methods, and discuss the remaining problems in character recognition that can be potentially solved by machine learning methods.

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maudtin

aerial

Off-line Writer Identification and Verification Using Gaussian Mixture Models,tification and the verification task. Three types of confidence measures are defined on the scores: simple score based, cohort model based, and world model based confidence measures. Experiments demonstrate a very good performance of the system on the identification and the verification task.

ROOF

Militia

江湖騙子

Structure Extraction in Printed Documents Using Neural Approaches,scussed in general terms: data-driven and model-driven. In the latter, some specific approaches like rule-based or formal grammar are usually studied on very stereotyped documents providing honest results, while in the former artificial neural networks are often considered for small patterns with go