This is a network for text recognition scenario. It consists of ResNext50-like backbone (stage-1-2) and bidirectional LSTM encoder-decoder. The network is able to recognize case-insensitive alphanumeric text (36 unique symbols).
-> openvino
| Metric | Value |
|---|---|
| Accuracy on the alphanumeric subset of ICDAR13 | 0.8828 |
| Text location requirements | Tight aligned crop |
| GFlops | 0.2726 |
| MParams | 1.4187 |
| Source framework | PyTorch |
Input tensor is imgs.
Shape: 1, 1, 32, 120 - An input image in the format B, C, H, W,
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.
The net outputs 2 blobs
logitswith the shape30, 1, 37in the formatW, B, L, where:- W - output sequence length
- B - batch size
- L - confidence distribution across alphanumeric symbols: "#0123456789abcdefghijklmnopqrstuvwxyz", where # - special blank character for CTC decoding algorithm.
The network output can be decoded by CTC Greedy Decoder or CTC Beam Search decoder.
Model is supported by text-detection c++ demo. In order to use this model in the demo, user should pass the following options:
-tr_pt_first
tr_o_blb_nm "logits"
For more information, please, see documentation of the demo.
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