Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Collection and Analysis of On-line Handwritten Japanese Character Patterns
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Handwritten Chinese Character Recognition: Alternatives to Nonlinear Normalization
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Online Recognition of Chinese Characters: The State-of-the-Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Normalization-Cooperated Gradient Feature Extraction for Handwritten Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
HCL2000 - A Large-scale Handwritten Chinese Character Database for Handwritten Character Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Recognition of handwritten Chinese characters by critical region analysis
Pattern Recognition
Regularized margin-based conditional log-likelihood loss for prototype learning
Pattern Recognition
Handwritten Chinese character recognition: effects of shape normalization and feature extraction
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
International Journal on Document Analysis and Recognition - Special Issue on Performance Evaluation
ICDAR 2011 Chinese Handwriting Recognition Competition
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
CASIA Online and Offline Chinese Handwriting Databases
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
MQDF Discriminative Learning Based Offline Handwritten Chinese Character Recognition
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
International Journal on Document Analysis and Recognition - Special issue - Selected and extended papers from ICDAR2009
Discriminative learning quadratic discriminant function for handwriting recognition
IEEE Transactions on Neural Networks
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Recently, the Institute of Automation of Chinese Academy of Sciences (CASIA) released the unconstrained online and offline Chinese handwriting databases CASIA-OLHWDB and CASIA-HWDB, which contain isolated character samples and handwritten texts produced by 1020 writers. This paper presents our benchmarking results using state-of-the-art methods on the isolated character datasets OLHWDB1.0 and HWDB1.0 (called DB1.0 in general), OLHWDB1.1 and HWDB1.1 (called DB1.1 in general). The DB1.1 covers 3755 Chinese character classes as in the level-1 set of GB2312-80. The evaluated methods include 1D and pseudo 2D normalization methods, gradient direction feature extraction from binary images and from gray-scale images, online stroke direction feature extraction from pen-down trajectory and from pen lifts, classification using the modified quadratic discriminant function (MQDF), discriminative feature extraction (DFE), and discriminative learning quadratic discriminant function (DLQDF). Our experiments reported the highest test accuracies 89.55% and 93.22% on the HWDB1.1 (offline) and OLHWDB1.1 (online), respectively, when using the MQDF classifier trained with DB1.1. When training with both the DB1.0 and DB1.1, the test accuracies on HWDB1.1 and OLHWDB are improved to 90.71% and 93.95%, respectively. Using DFE and DLQDF, the best results on HWDB1.1 and OLHWDB1.1 are 92.08% and 94.85%, respectively. Our results are comparable to the best results of the ICDAR2011 Chinese Handwriting Recognition Competition though we used less training samples.