Recognition of Handwritten ZIP Codes in a Real-World Non-Standard-Letter Sorting System

• Authors: Marcus Pfister, Sven Behnke, and Raul Rojas

 
• Applied Intelligence, vol. 12, num. (1-2), pp. 95-114, 2000.

 
• Abstract:
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 AG.
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 digit recognition

 
• Full paper: ai00.pdf