From Reading ZIP Codes to Fighting Bio-terrorism -
the Handwriting is on the Wall!

Venu Govindaraju
Center of Excellence for Document Analysis and Recognition (CEDAR)
Department of Computer Science and Engineering
State University of New York at Buffalo


Handwriting recognition is a mature field today with many applications such as processing of mail pieces, bank checks, census forms, medical forms, and more. We will present two of these applications: the first is the postal application where the handwriting technology has found the maximum success and the second is reading data on handwritten medical forms which is the most topical.

Postal automation represents a fertile area for the application of image processing and pattern recognition techniques. The US Postal service processes over 400 million pieces of letter mail a day and about 10-15% of these are handwritten. This makes handwritten address interpretation an attractive economic proposition requiring solutions to many challenging pattern recognition tasks. We will describe the postal automation process, the current read rates, the savings realized by the US Postal Service, and the challenges remaining. We will focus on 3 of the many different handwriting recognition technologies developed in our labs which are now part of the postal automation technologies in Australia and UK as well.

Active character recognition methods wherein the features used, the length of the feature vectors, and the amount of computational resources used are all dynamically determined based on the context. A system for rapid verification of unconstrained handwritten phrases using perceptual holistic features of the handwritten phrase images. A sequential method for combining word recognizers wherein the individual recognizers in the cascade are dynamically chosen based on a method of predicting the performance of a recognizer given the lexicon and the quality of the image.

We will conclude with a brief overview of a topical application of the same core handwriting recognition technology that is helping in automation of the collection of data on all patients who enter the emergency medical systems. If an automated analysis of the emergency data shows that many patients from the same geographical area are reporting the same symptoms in a short period of time, this critical information, which may not be obvious to the ER staff given the volume of patients that go through the system each day, could be disseminated easily and quickly to the appropriate authorities.

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