Thomas Fontaine and Lokendra Shastri,
Character Recognition Using a Modular Spatiotemporal Connectionist Model.
Tech. Rep. MS-CIS-92-24, University of Pennsylvania, Philadelphia, PA 19104-6389, March 1992.
Thomas Fontaine and Lokendra Shastri,
Character Recognition Using a Modular Spatiotemporal Connectionist Model.
Tech. Rep. MS-CIS-92-24, University of Pennsylvania, Philadelphia, PA 19104-6389, March 1992.
We describe a connectionist model for recognizing handprinted characters. Instead of treating the input as a static signal, the image is scanned over time and converted into a time-varying signal. The temporalized image is processed by a spatiotemporal connectionist network suitable for dealing with time-varying signals. The resulting system offers several attractive features, including shift-invariance and inherent retention of local spatial relationships along the temporalized axis, a reduction in the number of free parameters, and the ability to process images of arbitrary length. Connectionist networks were chosen as they offer learnability, rapid recognition, and attractive commercial possibilities. A modular and structured approach was taken in order to simplify network construction, optimization and analysis. Results for handprinted digit recognition are among the best reported to date on a set of real-world ZIP code digit images, provided by the United States Postal Service. The system achieved a 99.1% recognition rate on the training set and a 96% recognition rate on the test set with no rejections. A 99.0% recognition rate on the test set was achieved when 14.6% of the images were rejected.