A python library and tools for online handwritten Chinese character recognition.
Run script/download.sh to download from CASIA ^[C.-L. Liu, F. Yin, D.-H. Wang, and Q.-F. Wang, “Online and offline handwritten Chinese character recognition: Benchmarking on new databases,” Pattern Recognition, vol. 46, no. 1, pp. 155–162, Jan. 2013.] and HIT-OR3C ^[S. Zhou, Q. Chen, and X. Wang, “HIT-OR3C: an opening recognition corpus for Chinese characters,” in Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, Boston Massachusetts USA: ACM, pp. 223–230, Jun. 2010.].
| train | valitation | test | |
|---|---|---|---|
| number | 913 | 114 | 114 |
| gb1 | gb2 | gbk | |
|---|---|---|---|
| ratio | 100% | 100% | 59.2% |
Although most of the pattern recognition methods have been discarded since the introduction of deep learning methods, methods based on such as 8-directional features have been found to improve the performance without increasing the number of parameters.
^[Z.-L. Bai and Q. Huo, “A study on the use of 8-directional features for online handwritten Chinese character recognition,” 2005.]
^[X. Liu, B. Hu, Q. Chen, X. Wu, and J. You, “Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 11, pp. 4637–4648, Nov. 2020.]
A similar version used in open source Gboard Physical Handwriting version (Japanese). Developers used both 8-directional features and MobileNet. See blog post (Apr 1, 2018) for more details.
^[X. Jiang, L. Liu, C. Shan, Y. Shen, X. Dong, and D. Li, “Recognizing Vector Graphics without Rasterization,” Nov. 2021.]
^[X.-Y. Zhang, F. Yin, Y.-M. Zhang, C.-L. Liu, and Y. Bengio, “Drawing and Recognizing Chinese Characters with Recurrent Neural Network,” Jun. 2016.] ^[V. Carbune et al., “Fast Multi-language LSTM-based Online Handwriting Recognition,” Jan. 2020.]
^[Y. Li et al., “Fast and Robust Online Handwritten Chinese Character Recognition With Deep Spatial and Contextual Information Fusion Network,” IEEE Transactions on Multimedia, vol. 25, pp. 2140–2152, 2023.]