text-recognition-0012

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 alphanumeric text (36 unique symbols).

Example

_images/openvino.jpg

-> openvino

Specification

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*

Inputs

Image, name: Placeholder, shape: 1, 1, 32, 120 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.

Outputs

The net output is a blob with the shape 30, 1, 37 in the format W, 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.

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 alphanumeric text (36 unique symbols).

Example

_images/openvino.jpg

-> openvino

Specification

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*

Inputs

Image, name: Placeholder, shape: 1, 1, 32, 120 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.

Outputs

The net output is a blob with the shape 30, 1, 37 in the format W, 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.

Legal Information

[*] Other names and brands may be claimed as the property of others.