This is a network for handwritten Japanese text recognition scenario. It consists of a VGG16-like backbone, reshape layer and a fully connected layer. The network is able to recognize Japanese text consisting of characters in the Kondate and Nakayosi datasets.
-> 菊池朋子
| Metric | Value |
|---|---|
| GFlops | 117.136 |
| MParams | 15.31 |
| Accuracy on Kondate test set and test set generated from Nakayosi | 98.16% |
| Source framework | PyTorch* |
This demo adopts label error rate as the metric for accuracy.
Grayscale image, name - actual_input, shape - 1, 1, 96, 2000, format is B, C, H, W, where:
B- batch sizeC- number of channelsH- image heightW- image width
NOTE: the source image should be resized to specific height (such as 96) while keeping aspect ratio, and the width after resizing should be no larger than 2000 and then the width should be right-bottom padded to 2000 with edge values.
Name - output, shape - 186, 1, 4442, format is W, B, L, where:
W- output sequence lengthB- batch sizeL- confidence distribution across the supported symbols in Kondate and Nakayosi.
The network output can be decoded by CTC Greedy Decoder.
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
[*] Other names and brands may be claimed as the property of others.