Template-based Writer-independent Online Character Recognition System using Elastic Matching
نویسنده
چکیده
A writer independent handwriting recognition system must be able to recognize a wide variety of handwriting styles, while attempting to obtain a high degree of recognition accuracy. As the number of writing styles increases, so does the variability of data distribution. We describe here a template-based system using a string matching distance measure of linear time complexity for the recognition of online, mixed style (fluent and regular mixed) handwritten characters. Experimental results are given for a test set of 1696 uppercase letters, 1825 lowercase letters and 564 digits taken from 20 writers. Our results show 95.05%, 90.30% and 98.94% recognition accuracy for uppercase letters, lowercase letters and digits respectively. Our results are better as compared to best results available in literature for this category of characters.
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