Paper
16 January 2006 Address block features for image-based automated mail orientation
M. Shahab Khan, Hrishikesh B. Aradhye, Wayne T. Cruz
Author Affiliations +
Proceedings Volume 6067, Document Recognition and Retrieval XIII; 60670A (2006) https://doi.org/10.1117/12.655716
Event: Electronic Imaging 2006, 2006, San Jose, California, United States
Abstract
When mixed mail enters a postal facility, it must first be faced and oriented so that the address is readable by automated mail processing machinery. Existing US Postal Service (USPS) automated systems face and orient domestic mail by searching for fluorescing stamps on each mail piece. However, misplaced or partially fluorescing postage causes a significant fraction of mail to be rejected. Previously, rejected mail had to be faced and oriented by hand, thus increasing mail processing cost and time. Our earlier work successfully demonstrated the utility of machine-vision-based extraction of postal delimiters-such as cancellation marks and barcodes-for camera-based mail facing and orientation. Arguably, of all the localized information sources on the envelope image, the destination address block is the richest in content and the most structured in its form and layout. This paper focuses exclusively on the destination address block image and describes new vision-based features that can be extracted and used for mail orientation. Our results on real USPS datasets indicate robust performance. The algorithms described herein will be deployed nationwide on USPS hardware in the near future.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
M. Shahab Khan, Hrishikesh B. Aradhye, and Wayne T. Cruz "Address block features for image-based automated mail orientation", Proc. SPIE 6067, Document Recognition and Retrieval XIII, 60670A (16 January 2006); https://doi.org/10.1117/12.655716
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KEYWORDS
Feature extraction

Image processing

Cameras

Error analysis

Image processing algorithms and systems

Image segmentation

Optical character recognition

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