Abstract
Writer-specific character writing variations such as those of stroke order and stroke number are an important source of variability in the input when handwriting is captured “online” via a stylus and a challenge for robust online recognition of handwritten characters and words. It has been shown by several studies that explicit modeling of character allographs is important for achieving high recognition accuracies in a writer-independent recognition system. While previous approaches have relied on unsupervised clustering at the character or stroke level to find the allographs of a character, in this article we propose the use of constrained clustering using automatically derived domain constraints to find a minimal set of stroke clusters. The allographs identified have been applied to Devanagari character recognition using Hidden Markov Models and Nearest Neighbor classifiers, and the results indicate substantial improvement in recognition accuracy and/or reduction in memory and computation time when compared to alternate modeling techniques.
Published Version
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