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Proceedings Paper

Classification of photographed document images based on deep-learning features
Author(s): Guoqiang Zhong; Hui Yao; Yutong Liu; Chen Hong; Tuan Pham
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Paper Abstract

In this paper, we propose two new problems related to classification of photographed document images, and based on deep learning methods, present the baseline solutions for these two problems. The first problem is that, for some photographed document images, which book do they belong to? The second one is, for some photographed document images, what is the type of the book they belong to? To address these two problems, we apply “AexNet” to the collected document images. Using the pre-trained “AlexNet” on the ImageNet data set directly, we obtain 92.57% accuracy for the book-name classification and 93.33% accuracy for the book-type one. After fine-tuning on the training set of the photographed document images, the accuracy of the book-name classification increases to 95.54% and that of the booktype one to 95.42%. To our best knowledge, although there exist many image classification algorithm, no previous work has targeted to these two challenging problems. In addition, the experiments demonstrate that deep-learning features outperform features extracted with traditional image descriptors on these two problems.

Paper Details

Date Published: 8 February 2017
PDF: 6 pages
Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102250X (8 February 2017); doi: 10.1117/12.2266984
Show Author Affiliations
Guoqiang Zhong, Ocean Univ. of China (China)
Hui Yao, Ocean Univ. of China (China)
Yutong Liu, Ocean Univ. of China (China)
Chen Hong, Ocean Univ. of China (China)
Tuan Pham, Linkoping Univ. (China)

Published in SPIE Proceedings Vol. 10225:
Eighth International Conference on Graphic and Image Processing (ICGIP 2016)
Yulin Wang; Tuan D. Pham; Vit Vozenilek; David Zhang; Yi Xie, Editor(s)

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