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 case-insensitive alpha-numeric text (36 unique symbols).


-> openvino


Metric Value
Accuracy on the alphanumeric subset of ICDAR13 0.8818
Text location requirements Tight aligned crop
GFlops 1.485
MParams 5.568
Source framework TensorFlow



Shape: [1x1x32x120] - 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.


The net outputs a blob with the shape [30, 1, 37] in the format [WxBxL], where:

  • W - output sequence length
  • B - batch size
  • L - confidence distribution across alpha-numeric 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.

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