# 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).

-> 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, 32, 120, 1 in the format B, H, W, C, where:

• B - batch size

• H - image height

• W - image width

• C - number of channels

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.

## Demo usage¶

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities: