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Proceedings Paper

Sequence recognition of natural scene house number based on convolutional neural network
Author(s): Juping Zhong; Jing Gao; Guoxin Fang; Huimin Zhao; Jun Li
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Paper Abstract

Extracting character information from complex images has always been a research hotspot and a difficult topic in the field of computer vision. Natural scene number is severely distorted due to blurred image, uneven illumination, weak illumination, which makes it difficult to achieve ideal results for character recognition, especially identifying characters of arbitrary length. In this paper, we use the convolutional network to automatically extract the advantages of features, and construct a convolutional neural network that recognizes single digits. In order to highlight important features, we also use grayscale methods to weaken the background information in natural scenes and apply certain Proportional Dropout strategy to prevent overfitting. We use a cyclic network to generate character sequences and construct a deep convolutional neural network that recognizes sequence numbers and without split character characters. We construct a deep convolutional neural network that uses convolutional networks and cyclic network fusion to simultaneously identify multiple digits. We verify on the SVHN data set, we achieve better results in accuracy, we get the recognition rate of single digital house number is 95.72%, better than most algorithms in existing articles and the recognition rate of serial digital house number is 89.14%.

Paper Details

Date Published: 14 August 2019
PDF: 8 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111791T (14 August 2019); doi: 10.1117/12.2539868
Show Author Affiliations
Juping Zhong, Guangdong Polytechnic Normal Univ. (China)
Jing Gao, Guangdong Hengdian Information Technology Co. Ltd. (China)
Guoxin Fang, Guangdong Hengdian Information Technology Co. Ltd. (China)
Huimin Zhao, Guangdong Polytechnic Normal Univ. (China)
Jun Li, Guangdong Polytechnic Normal Univ. (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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