Offline Handwritten Chinese Character Recognition based on GoogLeNet and AlexNet
- Training Data : CASIA-HWDB1.0-1.2 and FlexiFont DataSets (Class = 7354)
- Testing Data : Chinese Handwriting Recognition Competition in ICDAR2013 (Class = 3755)
- AlexNet input size is 108 × 108; GoogLeNet input size is 112 × 112
- HCCR-AlexNet Caffemodel can be download from here
- Test accuracy on Chinese Handwriting Recognition Competition in ICDAR2013
| Network | Top-1 | Top-2 | Top-5 | Top-10 |
|---|---|---|---|---|
| AlexNet | 0.938437 | 0.975073 | 0.990790 | 0.995370 |
| GoogLeNet | 0.953227 | 0.982650 | 0.993464 | 0.996728 |
- Test accuracy vs. Iters (GoogLeNet)
- Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networksx[C]//Advances in neural information processing systems. 2012: 1097-1105.
- Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 1-9.
- Zhong Z, Jin L, Xie Z. High performance offline handwritten Chinese character recognition using GoogLeNet and directional feature maps[C]//Document Analysis and Recognition (ICDAR), 2015 13th International Conference on. IEEE, 2015: 846-850.
- Yin F, Wang Q F, Zhang X Y, et al. ICDAR 2013 Chinese handwriting recognition competition[C]//2013 12th International Conference on Document Analysis and Recognition. IEEE, 2013: 1464-1470.