A System for Mailpiece ZIP Code Assignment through Contextual Analysis. Phase 2

Abstract

This report describes the continued development and testing of a system for contextual analysis of machine printed address block images. The system receives a binary image of the address block (location of the address block is not a part of this work) and then: (1) segments the image into lines, words, and characters, (2) parses the address block to assign roles to the various words, (3) Forms queries based on number of words in the street name, word lengths, identification of suffix and directional words, and (externally supplied) knowledge the ZIP code on the mailpiece, (4) retrieves street records from a postal directory, (5) matches the street names from the directory to the word images using a character confidence measure, (6) given a close match uses (externally supplied) knowledge of the street number to assign a 4 digit add-on code. It should be stressed that this system does not use isolated character recognition to read words, but rather a lexicon based word verification system. The report emphasizes description of the segmentation processes, an automatic feature generation method used in the word verification system, and the structure of the postal database.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1991
Accession Number
ADA234571

Entities

People

  • Andrew M. Gillies
  • D. J. Hepp
  • J. M. Trenkle
  • M. P. Whalen

Organizations

  • Environmental Research Institute of Michigan

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Character Recognition
  • Computer Vision
  • Contractors
  • Data Sets
  • Detection
  • Detectors
  • Figure Of Merit
  • Hash Tables
  • Image Processing
  • Image Segmentation
  • Probability
  • Recognition
  • Statistics
  • Test Sets
  • United States
  • Verification Tests
  • Word Recognition

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Computer Vision.