The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
High performance offline handwritten Chinese character recognition using GoogLeNet and directional feature maps
Open accessAbstract
Just like its great success in solving many computer vision problems, the convolutional neural networks (CNN) provided new end-to-end approach to handwritten Chinese character recognition (HCCR) with very promising results in recent years. However, previous CNNs so far proposed for HCCR were neither deep enough nor slim enough. We show in this paper that, a deeper architecture can benefit HCCR a lot to achieve higher performance, meanwhile can be designed with less parameters. We also show that the traditional feature extraction methods, such as Gabor or gradient feature maps, are still useful for enhancing the performance of CNN. We design a streamlined version of GoogLeNet [13], which was original proposed for image classification in recent years with very deep architecture, for HCCR (denoted as HCCR-GoogLeNet). The HCCR-GoogLeNet we used is 19 layers deep but involves with only 7.26 million parameters. Experiments were conducted using the ICDAR 2013 offline HCCR competition dataset. It has been shown that with the proper incorporation with traditional directional feature maps, the proposed single and ensemble HCCR-GoogLeNet models achieve new state of the art recognition accuracy of 96.35% and 96.74%, respectively, outperforming previous best result with significant gap.
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Cited by
Luan, S., Chen, C., Zhang, B., Han, J., & Liu, J. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2835143 · Cited by 428
Drawing and Recognizing Chinese Characters with Recurrent Neural Network
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Fan, X., Yao, Q., Cai, Y., Miao, F., Sun, F., & Li, Y. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2858789 · Cited by 323
Data augmentation for motor imagery signal classification based on a hybrid neural network.
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Metadata
- Authors
- Zhong, Z., Jin, L., & Xie, Z.
- Venue
- 2015 13th International Conference on Document Analysis and Recognition (ICDAR)
- Publisher
- IEEE
- Published
- 2015-08
- Pages
- 846-850
- ISSN
- 1520-5363 (ISSN-L)
- OpenAlex
- W2170866695
- Cited by
- 309
- References
- 19
- FWCI
- 10.53
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zhong, Z., Jin, L., & Xie, Z. (2015). High performance offline handwritten Chinese character recognition using GoogLeNet and directional feature maps. 2015 13th International Conference on Document Analysis and Recognition (ICDAR). https://doi.org/10.1109/icdar.2015.7333881
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