The U.S. Postal Service (USPS) is investing heavily in
research and development of automated mail handling systems. A
major component in these systems is the use of Optical Character
Recognition (OCR) to read the destination address and ZIP Code,
and then bar code the mail piece. High speed sorting equipment
can then sort the mail using the bar code. Current USPS
OCR/automated mail handling systems only process letter mail (no
automated address-reading systems exist for nonletter mail).
Moreover, these OCR systems only capture and read a restricted
field-of-view image. Letters can be rejected by these OCR
systems because of nonstandard address location (outside the
field-of-view), skewed address lines, or handwritten addresses.
Current research is working toward building OCR systems capable
of processing all forms of mail which include letters, flats, and
irregular parcel and pieces (IPPs). These systems must scan an
entire mail image for the destination address block which can
assume any orientation. For nonletter mail, such as magazines,
this is an exceedingly difficult task, since the entire face of
up to 11 by 14 inches must be searched, and the address block
must be chosen from all the other extraneous nonaddress
information.
This paper details an experimental address block location
system developed at MITRE. The" system uses a backpropagation
neural network trained to discriminate the frequency
characteristics of address blocks from other candidates. The
current system is trained on magazine flat mail.
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