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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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发表于 2025-3-21 20:09:52 | 显示全部楼层 |阅读模式
书目名称Camera-Based Document Analysis and Recognition
副标题5th International Wo
编辑Masakazu Iwamura,Faisal Shafait
视频videohttp://file.papertrans.cn/221/220957/220957.mp4
概述Fast track conference proceedings.Unique visibility.State of the art research.Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面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
关键词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
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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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