Automatic Chinese Postal Address Block Location Using Component-wise Proximity Descriptors and Random Forests

X Dong, J Dong, Huiyu Zhou, J. Sun, D Tao

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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 (CPRF). 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.
Original languageEnglish
JournalIEEE Transactions on Industrial Electronics
Early online date19 Oct 2017
DOIs
Publication statusEarly online date - 19 Oct 2017

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