Automatic Chinese Postal Address Block Location Using Proximity Descriptors and Cooperative Profit Random Forests.pdf (1.64 MB)
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Automatic Chinese Postal Address Block Location Using Proximity Descriptors and Cooperative Profit Random Forests

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journal contribution
posted on 16.02.2018, 09:56 by Xinghui Dong, Junyu Dong, Huiyu Zhou, Jianyuan Sun, Dacheng Tao
Locating the destination address block is key to automated sorting of mails. Due to the characteristics of Chinese envelopes used in mainland China, we here exploit proximity cues in order to describe the investigated regions on envelopes. We propose two proximity descriptors encoding spatial distributions of the connected components obtained from the binary envelope images. To locate the destination address block, these descriptors are used together with cooperative profit random forests (CPRFs). Experimental results show that the proposed proximity descriptors are superior to two component descriptors, which only exploit the shape characteristics of the individual components, and the CPRF classifier produces higher recall values than seven state-of-the-art classifiers. These promising results are due to the fact that the proposed descriptors encode the proximity characteristics of the binary envelope images, and the CPRF classifier uses an effective tree node split approach.

Funding

The work of J. Dong was supported by the National Natural Science Foundation of China under Grant 61271405 and Grant 41576011. The work of H. Zhou was supported in part by the UK Engineering and Physical Sciences Research Council under Grant EP/N508664/1, Grant EP/R007187/1, and Grant EP/N011074/1, and in part by the Royal Society-Newton Advanced Fellowship under Grant NA160342.

History

Citation

IEEE Transactions on Industrial Electronics, 2018, 65 (5), pp. 4401-4412 (12)

Author affiliation

/Organisation

Version

AM (Accepted Manuscript)

Published in

IEEE Transactions on Industrial Electronics

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

0278-0046

eissn

1557-9948

Acceptance date

03/10/2017

Copyright date

2017

Available date

16/02/2018

Publisher version

http://ieeexplore.ieee.org/document/8074799/

Language

en