Automatic Allograph Categorization Based on Stroke Clustering for On-Line Handwritten Japanese Character Recognition

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Venue:
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Year:
1998

Citing 3
Cited 1

Finding structure in diversity: a hierarchical clustering method for the categorization of allographs in handwriting

ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
On-line recognition of run-on Korean characters

ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
A set of handwriting families: style recognition

ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1

Writer Adaptation for Online Handwriting Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

To allow the construction of a recognition dictionary that includes various writing styles, an automatic method for categorizing writing styles of characters (allographs) is proposed. In the first step of allograph categorization, handwritten strokes contained in training data are categorized to obtain prototype strokes. These strokes are used to categorize handwritten characters and thus obtain allographs. In this approach, allographs share common prototype strokes. This makes it possible to reduce the dictionary size and computation time needed for recognition. Allograph dictionaries for 2321 categories were experimentally made by using handwritten characters produced by 121 writers. Recognition experiments using these dictionaries were carried out to determine the relationship between the number of allographs and the recognition accuracy.