The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
ICDAR 2013 Chinese Handwriting Recognition Competition
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.
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Multi-column deep neural networks for image classification
Ciresan, D., Meier, U., & Schmidhuber, J. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248110 · Cited by 3,720
Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition
Kimura, F., Takashina, K., Tsuruoka, S., & Miyake, Y. (1987) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1987.4767881 · Cited by 501
Online and offline handwritten Chinese character recognition: Benchmarking on new databases
Liu, C.-L., Yin, F., Wang, D.-H., & Wang, Q.-F. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2012.06.021 · Cited by 326
Self-citationOffline recognition of unconstrained handwritten texts using HMMs and statistical language models
Horst Bunke, A., Samy Bengio, A., & Vinciarelli, A. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.14 · Cited by 291
Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields
Zhou, X., Wang, D., Tian, F., Liu, C., & Nakagawa, M. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.49 · Cited by 86
Self-citationICDAR 2011 Chinese Handwriting Recognition Competition
Liu, C., Yin, F., Wang, Q., & Wang, D.-H. (2011) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2011.291 · Cited by 81
Self-citationChinese Handwriting Recognition Contest 2010
Liu, C., Yin, F., Wang, D.-H., & Wang, Q. (2010) · Chinese Conference on Pattern Recognition · doi:10.1109/ccpr.2010.5659229 · Cited by 47
Self-citationMQDF Discriminative Learning Based Offline Handwritten Chinese Character Recognition
Wang, Y., Ding, X., & Liu, C. (2011) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2011.222 · Cited by 23
Self-citationGe, Y., Huo, Q., & Feng, Z. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2002.5743976 · Cited by 22
Chinese Handwriting Recognition: An Algorithmic Perspective
Su, T. (2013) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-642-31812-2 · Cited by 22
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Loey, M., Smarandache, F., & M. Khalifa, N. E. (2020) · Symmetry · doi:10.3390/sym12040651 · Cited by 538
“[28,29] showed how CNNs based on Graphics Processing Unit (GPU) can enhance many vision benchmark records such as MNIST [30], Chinese characters [31], Arabic digits recognition [32], Arabic handwritten characters recognition [33], NORB (jittered, cluttered) [34], tra ffi c signs [35], and large-scale…”
BackgroundRobust Classification with Convolutional Prototype Learning
Yang, H.-M., Zhang, X.-Y., Yin, F., & Liu, C.-L. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00366 · Cited by 469
Self-citationDrawing and Recognizing Chinese Characters with Recurrent Neural Network
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Metadata
- Authors
- Yin, F., Wang, Q.-F., Zhang, X.-Y., & Liu, C.-L.
- Venue
- 2013 12th International Conference on Document Analysis and Recognition
- Publisher
- IEEE
- Published
- 2013-08
- Pages
- 1464-1470
- ISSN
- 1520-5363 (ISSN-L)
- OpenAlex
- W1978964824
- Cited by
- 324
- References
- 11
- FWCI
- 12.16
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Yin, F., Wang, Q.-F., Zhang, X.-Y., & Liu, C.-L. (2013). ICDAR 2013 Chinese Handwriting Recognition Competition. 2013 12th International Conference on Document Analysis and Recognition. https://doi.org/10.1109/icdar.2013.218
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