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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).

Example

-> 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:

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