DocumentCode :
3490502
Title :
ICDAR 2013 Chinese Handwriting Recognition Competition
Author :
Fei Yin ; Qiu-Feng Wang ; Xu-Yao Zhang ; Cheng-Lin Liu
Author_Institution :
Nat. Lab. of Pattern Recognition (NLPR), Inst. of Autom., Beijing, China
fYear :
2013
fDate :
25-28 Aug. 2013
Firstpage :
1464
Lastpage :
1470
Abstract :
This paper describes the Chinese handwriting recognition competition held at the 12th International Conference on Document Analysis and Recognition (ICDAR 2013). This third competition in the series again used the CASIA-HWDB/OLHWDB databases as the training set, and all the submitted systems were evaluated on closed datasets to report character-level correct rates. This year, 10 groups submitted 27 systems for five tasks: classification on extracted features, online/offline isolated character recognition, online/offline handwritten text recognition. The best results (correct rates) are 93.89% for classification on extracted features, 94.77% for offline character recognition, 97.39% for online character recognition, 88.76% for offline text recognition, and 95.03% for online text recognition, respectively. In addition to the test results, we also provide short descriptions of the recognition methods and brief discussions on the results.
Keywords :
feature extraction; handwriting recognition; handwritten character recognition; image classification; CASIA-HWDB database; ICDAR 2013 Chinese handwriting recognition competition; International Conference on Document Analysis and Recognition; OLHWDB database; extracted feature classification task; online-offline handwritten text recognition task; online-offline isolated character recognition task; recognition methods; Character recognition; Databases; Educational institutions; Feature extraction; Handwriting recognition; Text recognition; Training; CASIA-HWDB/OLHWDB database; Chinese handwriting recognition competition; handwritten text recognition; isolated character recongition; offline; online;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Document Analysis and Recognition (ICDAR), 2013 12th International Conference on
Conference_Location :
Washington, DC
ISSN :
1520-5363
Type :
conf
DOI :
10.1109/ICDAR.2013.218
Filename :
6628856
Link To Document :
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