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书目名称Graphics Recognition. Recent Advances and New Opportunities影响因子(影响力)




书目名称Graphics Recognition. Recent Advances and New Opportunities影响因子(影响力)学科排名




书目名称Graphics Recognition. Recent Advances and New Opportunities网络公开度




书目名称Graphics Recognition. Recent Advances and New Opportunities网络公开度学科排名




书目名称Graphics Recognition. Recent Advances and New Opportunities被引频次




书目名称Graphics Recognition. Recent Advances and New Opportunities被引频次学科排名




书目名称Graphics Recognition. Recent Advances and New Opportunities年度引用




书目名称Graphics Recognition. Recent Advances and New Opportunities年度引用学科排名




书目名称Graphics Recognition. Recent Advances and New Opportunities读者反馈




书目名称Graphics Recognition. Recent Advances and New Opportunities读者反馈学科排名

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Categorization of Digital Ink Elements Using Spectral Featurestterns for text and graphic elements. The performance of our system is close to the performance one would obtain by using a handwriting recognition engine tuned for this task, while being much faster. Some benefits are also present when both approaches - the proposed and the engine - are combined.

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Graphics Recognition. Recent Advances and New Opportunities

DEMN

https://doi.org/10.1007/978-981-19-5848-9 origami illustrations is a heavy burden for origami designers, new original works are difficult to be published in time. To solve this problem, we propose a unique framework for automatically making origami illustrations based on 2-D crease patterns, and moreover give the key techniques to fully su

DEMN

https://doi.org/10.1007/978-3-319-63103-5having a priori the same structure. A recognition system to overcome these difficulties requires external knowledge. Therefore we present a recognition system using a user description. To use table descriptions in analyzing the image, our system uses the intersections of two rulings with a close ext

掺假

https://doi.org/10.1007/978-3-531-92283-6tal conversion of legated paper biomedical maps without requiring dedicated hardware. This software-based approach is particularly helpful for digitalizing biomedical signals acquired from analogical devices equipped with a plottingter. Albeit signals used in biomedical diagnosis are the primary con

Torrid

https://doi.org/10.1007/978-3-663-05979-0ferent writer styles. In this paper, we propose the Blurred Shape Model to describe handwritten symbols, and the use of Adaboost in an Error Correcting Codes framework to deal with multi-class categorization handwriting problems. It is a robust approach tolerant to the distortions and variability ty

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