Use Case and High-Level Description
This is a network for text recognition scenario. It consists of VGG16-like backbone and bidirectional LSTM encoder-decoder. The network is able to recognize school marks that should have format either <digit> or <digit>.<digit> (e.g. 4 or 3.5).
Example
-> Mark2.5
Specification
| Metric | Value |
|---|---|
| Accuracy (internal test set) | 98.83% |
| Text location requirements | Tight aligned crop |
| GFlops | 0.792 |
| MParams | 5.555 |
| Source framework | TensorFlow |
Performance
Inputs
Shape: [1x1x32x64] - An input image in the format [BxCxHxW], where:
- B - batch size
- C - number of channels
- H - image height
- W - image width
Note that the source image should be tight aligned crop with detected text converted to grayscale.
Outputs
The net outputs a blob with the shape [16, 1, 13] in the format [WxBxL], where:
- W - output sequence length
- B - batch size
- L - confidence distribution across the alphabet:
"0123456789._#", where # - special blank character for CTC decoding algorithm and the character `'_'` replaces all non-numeric symbols.
The network output can be decoded by CTC Greedy Decoder or CTC Beam Search decoder.