An approach for real-time recognition of online Chinese handwritten sentences
Pattern Recognition
Pattern field classification with style normalized transformation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Maxi-Min discriminant analysis via online learning
Neural Networks
HBF49 feature set: A first unified baseline for online symbol recognition
Pattern Recognition
Unsupervised language model adaptation for handwritten Chinese text recognition
Pattern Recognition
Keyword spotting in unconstrained handwritten Chinese documents using contextual word model
Image and Vision Computing
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This paper introduces a pair of online and offline Chinese handwriting databases, containing samples of isolated characters and handwritten texts. The samples were produced by 1,020 writers using Anoto pen on papers for obtaining both online trajectory data and offline images. Both the online samples and offline samples are divided into six datasets, three for isolated characters (DB1.0-C1.2) and three for handwritten texts (DB2.0-C2.2). The (either online or offline) datasets of isolated characters contain about 3.9 million samples of 7,356 classes (7,185 Chinese characters and 171 symbols), and the datasets of handwritten texts contain about 5,090 pages and 1.35 million character samples. Each dataset is segmented and annotated at character level, and is partitioned into standard training and test subsets. The online and offline databases can be used for the research of various handwritten document analysis tasks.