# handwritten-score-recognition-0003¶

## 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 school marks that should have format either <digit> or <digit>.<digit> (e.g. 4 or 3.5).

-> Mark2.5

## Specification¶

Metric

Value

Accuracy (internal test set)

98.83%

Text location requirements

Tight aligned crop

GFlops

0.792

MParams

5.555

Source framework

TensorFlow*

## Inputs¶

Image, name: Placeholder, shape: 1, 32, 64, 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 outputs a blob with the shape 16, 1, 13 in the format W, B, L, where:

• W - output sequence length

• B - batch size

• L - 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.

## Demo usage¶

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