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書(shū)目名稱Graphics Recognition. Current Trends and Evolutions影響因子(影響力)




書(shū)目名稱Graphics Recognition. Current Trends and Evolutions影響因子(影響力)學(xué)科排名




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書(shū)目名稱Graphics Recognition. Current Trends and Evolutions被引頻次




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書(shū)目名稱Graphics Recognition. Current Trends and Evolutions讀者反饋




書(shū)目名稱Graphics Recognition. Current Trends and Evolutions讀者反饋學(xué)科排名

drusen

mechanical

nettle

https://doi.org/10.1007/978-0-387-71564-3This document summarizes the discussion of the interest group on Graphics Syntax in the Deep Learning Age that took place in the 12th IAPR International Workshop on Graphics Recognition (GREC).

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BURSA

Multikulturelle Demokratien im Vergleichses at different thermodynamic conditions and compositions for a wide variety of materials. For this work, we concentrate on approximately 80 thermodynamic phase diagrams of binary metallic alloy systems which give phase information of multi-component systems at varied temperatures and mixture ratio

BURSA

https://doi.org/10.1007/978-3-658-11510-4nstruction documents. It acts as a blueprint to provide the engineers or architects a bird’s-eye view of the whole project. Generally, an engineering project comprises of large number of document sheets. These sheets maintain the constructional hierarchy of the projects. Accessing the information ma

Anonymous

https://doi.org/10.1007/978-3-319-41111-8acts as a graphical language that helps to communicate ideas and information from one mind to another. A construction project generally contains huge number of such drawing documents. An engineer or architect often needs to refer various documents while creating a new one or marking irregularities i

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ormation in diagrams, scanned tables and other types of images, enriches the underlying concepts, but requires a classifier that can recognize the huge variability of potential embedded image types and enable their relationship reconstruction. Here we tested different deep learning-based approaches

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