Automatic Chinese Postal Address Block Location Using Proximity Descriptors and Cooperative Profit Random Forests
Authors: Dong, X., Dong, J., Zhou, H., Sun, J. and Tao, D.
Journal: IEEE Transactions on Industrial Electronics
Volume: 65
Issue: 5
Pages: 4401-4412
ISSN: 0278-0046
DOI: 10.1109/TIE.2017.2764866
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.
https://eprints.bournemouth.ac.uk/33297/
Source: Scopus
Automatic Chinese Postal Address Block Location Using Proximity Descriptors and Cooperative Profit Random Forests
Authors: Jianyuan, S., Xinghui, D., Junyu, D., Huiyu, Z. and Dacheng, T.
Journal: IEEE Transactions on Industrial Electronics
Publisher: IEEE
ISSN: 0278-0046
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 descriptorsencodingspatialdistributionsoftheconnectedcomponents 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 descriptorsaresuperiortotwo componentdescriptors,which onlyexploittheshapecharacteristicsoftheindividualcomponents,andtheCPRFclassifierproduceshigherrecallvaluesthan seven state-of-the-artclassifiers.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.
https://eprints.bournemouth.ac.uk/33297/
Source: Manual
Automatic Chinese Postal Address Block Location Using Proximity Descriptors and Cooperative Profit Random Forests.
Authors: Dong, X., Dong, J., Zhou, H., Sun, J. and Tao, D.
Journal: IEEE Transactions on Industrial Electronics
Volume: 65
Issue: 5
Pages: 4401-4412
ISSN: 0278-0046
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.
https://eprints.bournemouth.ac.uk/33297/
Source: BURO EPrints