Titlebook: Document Analysis and Recognition – ICDAR 2021; 16th International C Josep Lladós,Daniel Lopresti,Seiichi Uchida Conference proceedings 202

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书目名称Document Analysis and Recognition – ICDAR 2021影响因子(影响力)




书目名称Document Analysis and Recognition – ICDAR 2021影响因子(影响力)学科排名




书目名称Document Analysis and Recognition – ICDAR 2021网络公开度




书目名称Document Analysis and Recognition – ICDAR 2021网络公开度学科排名




书目名称Document Analysis and Recognition – ICDAR 2021被引频次




书目名称Document Analysis and Recognition – ICDAR 2021被引频次学科排名




书目名称Document Analysis and Recognition – ICDAR 2021年度引用




书目名称Document Analysis and Recognition – ICDAR 2021年度引用学科排名




书目名称Document Analysis and Recognition – ICDAR 2021读者反馈




书目名称Document Analysis and Recognition – ICDAR 2021读者反馈学科排名

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Document Analysis and Recognition – ICDAR 2021978-3-030-86337-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Theranostics for Breast Cancer Stem Cellss, as encountered in social networks, for detection and recognition of scene text. The proposed classifier efficiently removes non-text images from consideration, thus allowing to apply the potentially computationally heavy scene text detection and OCR on only a fraction of the images..The proposed

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Translational Medicine Researchifficult to use rectangular bounding boxes to detect text locations accurately. To detect multi-oriented text, rotated bounding box-based methods have been explored as an alternative. However, they are not as accurate for scene text detection as rectangular bounding box-based methods. In this paper,

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Translational Research in Strokebased on abundant labeled data for model training. Obtaining text images is a relatively easy process, but labeling them is quite expensive. To alleviate the dependence on labeled data, semi-supervised learning which combines labeled and unlabeled data seems to be a reasonable solution, and is prove

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