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Titlebook: Camera-Based Document Analysis and Recognition; 5th International Wo Masakazu Iwamura,Faisal Shafait Conference proceedings 2014 Springer I

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書目名稱Camera-Based Document Analysis and Recognition
副標(biāo)題5th International Wo
編輯Masakazu Iwamura,Faisal Shafait
視頻videohttp://file.papertrans.cn/221/220957/220957.mp4
概述Fast track conference proceedings.Unique visibility.State of the art research.Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Camera-Based Document Analysis and Recognition; 5th International Wo Masakazu Iwamura,Faisal Shafait Conference proceedings 2014 Springer I
描述.This book constitutes the thoroughly refereed post-workshop proceedings of the 5th International Workshop on Camera-Based Document Analysis and Recognition, CBDAR 2013, held in Washington, DC, USA, in August 2013. The 14 revised full papers presented were carefully selected during two rounds of reviewing and improvement from numerous original submissions. Intended to give a snapshot of the state-of-the-art research in the field of camera based document analysis and recognition, the papers are organized in topical sections on text detection and recognition in scene images and camera-based systems..
出版日期Conference proceedings 2014
關(guān)鍵詞book scanning; document analysis; network-based clustering; optical character recognition; text detectio
版次1
doihttps://doi.org/10.1007/978-3-319-05167-3
isbn_softcover978-3-319-05166-6
isbn_ebook978-3-319-05167-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2014
The information of publication is updating

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A Hierarchical Visual Saliency Model for Character Detection in Natural Scenes characters have different visual properties from their non-character neighbors, which make them salient. With this assumption, characters should response well to computational models of visual saliency. However in some situations, characters belonging to scene text mignt not be as salient as one mi
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Scene Text Detection via Integrated Discrimination of Component Appearance and Consensuspearance is modeled with an SVM based dictionary classifier and the component consensus is represented with color and spatial layout features. Responses of the dictionary classifier are integrated with the consensus features into a discriminative model, where the importance of features is determined
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