Recognition of Handwritten ZIP Codes in a Real-World Non-Standard-Letter
Authors: Marcus Pfister, Sven Behnke, and Raul Rojas
Applied Intelligence, vol. 12, num. (1-2), pp. 95-114, 2000.
In this article, we describe the OCR and image processing algorithms
used to read destination addresses from non-standard letters (flats) by
Siemens postal automation system currently in use by the Deutsche Post
We first describe the sorting machine, its OCR hardware and the sequence
of image processing and pattern recognition algorithms needed to solve
the difficult task of reading mail addresses, especially handwritten ones.
The article concentrates mainly on the two classifiers used to recognize
handprinted digits. One of them is a complex time delayed neural network
(TDNN) used to classify scaled digit-features. The other classifier extracts
the structure of each digit and matches it to a number of prototypes. Different
digits represented by the same graph are then discriminated by classifiying
some of the features of the digit-graph with small neural networks.
We also describe some approaches for the segmentation of the digits
in the ZIP code, so that the resulting parts can be processed and evaluated
by the classifiers.
Keywords: postal automation, address reading, neural networks, handprinted
Full paper: ai00.pdf
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