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A model-based computer vision system for recognizing handwritten ZIP codes

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Abstract

This paper describes a recognition system for handwritten ZIP Codes currently under development at the Environmental Research Institute of Michigan (ERIM). Included within this system are techniques for preprocessing address block images, locating ZIP Codes, splitting touching characters, and identifying handwritten numerals. These techniques rely on mathematical morphology-based image processing and on hierarchical matching of object models to symbolic image representations. The image processing uses adaptive filtering, thresholding, and skeletonizing to create binary and state-labeled images. The matching process uses these images and extensively developed handwritten digit models to identify ZIP Codes. The end-to-end system has been tested on 500 randomly selected address block images. The system correctly recognized a large portion of the ZIP Codes in the test images (45.0%), and incorrectly classified a very low percentage of isolated handwritten digits (0.9%). Overall performance continues to be improved through incremental digit model refinement.

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Authors and Affiliations

  1. Environmental Research Institute of Michigan, P.O. Box 8618, 48107, Ann Arbor, MI, USA

    Brian T. Mitchell & Andrew M. Gillies

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  1. Brian T. Mitchell
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  2. Andrew M. Gillies
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Additional information

This work was funded by the Office of Advanced Technology, United States Postal Service under contract 104230-86-H-0042.

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Mitchell, B.T., Gillies, A.M. A model-based computer vision system for recognizing handwritten ZIP codes. Machine Vis. Apps. 2, 231–243 (1989). https://doi.org/10.1007/BF01215877

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