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.
Video is an increasingly important and ever-growing source of information to the intelligence and homeland defense analyst. A capability to automatically identify the contents of video imagery would enable the analyst to index relevant foreign and domestic news videos in a convenient and meaningful way. To this end, the proposed system aims to help determine the geographic focus of a news story directly from video imagery by detecting and geographically localizing political maps from news broadcasts, using the results of videotext recognition in lieu of a computationally expensive, scale-independent shape recognizer. Our novel method for the geographic localization of a map is based on the premise that the relative placement of text superimposed on a map roughly corresponds to the geographic coordinates of the locations the text represents. Our scheme extracts and recognizes videotext, and iteratively identifies the geographic area, while allowing for OCR errors and artistic freedom. The fast and reliable recognition of such maps by our system may provide valuable context and supporting evidence for other sources, such as speech recognition transcripts. The concepts of syntax-directed content analysis of videotext presented here can be extended to other content analysis systems.