HIT-OR3C: an opening recognition corpus for Chinese characters
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
On-line handwriting recognition with parallelized machine learning algorithms
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
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This paper describes a publicly available database, CASIA-OLHWDB1, for research on online handwritten Chinese character recognition. This database is the first of our series of online/offline handwritten characters and texts, collected using Anoto pen on paper. It contains unconstrained handwritten characters of 4,037 categories (3,866 Chinese characters and 171 symbols) produced by 420 persons, and 1,694,741 samples in total. It can be used for design and evaluation of character recognition algorithms and classifier design for handwritten text recognition systems. We have partitioned the samples into three grades and into training and test sets. Preliminary experiments on the database using a state-of-the-art recognizer justify the challenge of recognition.