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, 1, 32, 64 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 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.

Legal Information

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