Template-based online character recognition

Handwriting is a common, natural form of communication for humans, and therefore it is useful to utilize this modality as a means of input to machines. One well-known method of classifying individual characters or words is template matching. We demonstrate a template-based system for online character recognition where the number of representative templates is determined automatically. These templates can be viewed as representing different styles of writing a particular character. The templates are then used as a reference for efficient classification using decision trees. Overall, our classifier achieves an 86.9% accuracy on a set of 17,928 alphanumeric characters (36 classes; 10 digits and 26 lowercase letters) with a throughput of over 8 characters per second on a 296 MHz Sun UltraSparc. (C) 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.