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doi:10.1016/0031-3203(93)90172-S 
Copyright © 1993 Published by Elsevier Science B.V.
Recognition experiments of cursive dynamic handwriting with self-organizing networks
Available online 19 May 2003.
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Abstract
The aim of the paper is to assess the feasibility of using self-organizing methods for the development of a recognition system of cursive, dynamic handwriting for interactive applications with a large dictionary. A prototype system based on segmentation into motoric strokes and concurrent segmentation/classification of allographs by means of a set of allographic maps is developed. In the initial implementation, based on Kohonen's SOMs (self-organized maps), a 70% user-specific word recognition rate with a 4k-words dictionary is approached. From this, indications are derived for a modified neural recognizer (SOC: self-organized classifier) that is still based on self-organization but is more flexible (dynamic network size and topology) and can support incremental learning. The new model, together with improved pre-processing methods, could overcome the 80% mark in a pilot study with three subjects.
Author Keywords: Cursive handwriting; Neural networks; Self-organization
