Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/41192
Title: Automatic Chinese Postal Address Block Location Using Proximity Descriptors and Cooperative Profit Random Forests
Authors: Dong, Xinghui
Dong, Junyu
Zhou, Huiyu
Sun, Jianyuan
Tao, Dacheng
First Published: 19-Oct-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: IEEE Transactions on Industrial Electronics, 2018, 65 (5), pp. 4401-4412 (12)
Abstract: 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.
DOI Link: 10.1109/TIE.2017.2764866
ISSN: 0278-0046
eISSN: 1557-9948
Links: http://ieeexplore.ieee.org/document/8074799/
http://hdl.handle.net/2381/41192
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2017, IEEE. Deposited with reference to the publisher’s open access archiving policy.
Appears in Collections:Published Articles, Dept. of Computer Science

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