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
).
Metric | Value |
---|---|
Accuracy (internal test set) | 98.83% |
Text location requirements | Tight aligned crop |
GFlops | 0.792 |
MParams | 5.555 |
Source framework | TensorFlow* |
Image, name: Placeholder
, shape: 1, 32, 64, 1
in the format B, H, W, C
, where:
B
- batch sizeH
- image heightW
- image widthC
- number of channels
Note that the source image should be tight aligned crop with detected text converted to grayscale.
The net outputs a blob with the shape 16, 1, 13
in the format W, B, L
, where:
W
- output sequence lengthB
- batch sizeL
- 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.
[*] Other names and brands may be claimed as the property of others.