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2015 13th International Conference on Document Analysis and Recognition (ICDAR) (2015)
Tunis, Tunisia
Aug. 23, 2015 to Aug. 26, 2015
ISBN: 978-1-4799-1804-1
pp: 801-805
Xiao Liu , College of Computer Science, Zhejiang University, Hangzhou, China 310027
Binbin Tang , College of Computer Science, Zhejiang University, Hangzhou, China 310027
Zhenyang Wang , College of Computer Science, Zhejiang University, Hangzhou, China 310027
Xianghua Xu , College of Computer Science, Hangdian University, Hangzhou, China 310027
Shiliang Pu , HIK VISION, Hangzhou, China 310027
Dapeng Tao , School of Information Science and Engineering, Yunnan University, Kunming, China 650000
Mingli Song , College of Computer Science, Zhejiang University, Hangzhou, China 310027
ABSTRACT
Chart classification is the foundation of chart analysis and document understanding. In this paper, we propose a novel framework to classify charts by combining convolutional networks and deep belief networks. In the framework, we firstly extract deep hidden features of charts, which are taken from the fully-connected layer of deep convolutional networks. We then utilize deep belief networks to predict the labels of the charts based on their deep hidden features. The convolutional networks are initialized using a large number of natural images and fine-tuned using the chart images to prevent overfitting. Compared with previous methods using primitive feature extraction, the deep features give our framework better scalability and stability. We collect a 5-class chart dataset with more than 5000 images and show that the proposed framework outperforms existing methods greatly.
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CITATION

X. Liu et al., "Chart classification by combining deep convolutional networks and deep belief networks," 2015 13th International Conference on Document Analysis and Recognition (ICDAR), Tunis, Tunisia, 2015, pp. 801-805.
doi:10.1109/ICDAR.2015.7333872
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