Skip to content

Latest commit

 

History

History
94 lines (63 loc) · 3.13 KB

README.md

File metadata and controls

94 lines (63 loc) · 3.13 KB
Raw

text-recognition-0015 (composite)

Use Case and High-Level Description

This is a text-recognition composite model that recognizes scene text. The model uses predefined set of alphanumeric symbols (case-sensitive) to predict words. The model is built on the ResNeXt-101 backbone with additional 2d attention-based text recognition head.

Example of the input data

Example of the output

openvino

Composite model specification

Metric Value
Accuracy on the alphanumeric subset of ICDAR13 0.8995
Accuracy on the alphanumeric subset of ICDAR03 0.9389
Accuracy on the alphanumeric subset of ICDAR15 0.7355
Accuracy on the alphanumeric subset of SVT 0.8764
Accuracy on the alphanumeric subset of IIIT5K 0.8413
Text location requirements Tight aligned crop
Source framework PyTorch*

The above accuracies are calculated for case-insensitive mode (i.e. GT text and predicted text are all casted to lowercase).

Encoder model specification

The text-recognition-0015-encoder model is a ResNeXt-101 like backbone with convolutional encoder part of the text recognition.

Metric Value
GFlops 12.4
MParams 398

Inputs

Image, name: imgs, shape: 1, 1, 64, 256 in the 1, C, H, W format, where:

  • C - number of channels
  • H - image height
  • W - image width

Outputs

  1. Name: decoder_hidden, shape: 1, 1, 1024. Initial context state of the GRU cell.
  2. Name: features, shape: 1, 16, 1024. Features from encoder part of text recognition head.

Decoder model specification

The text-recognition-15-decoder model is a GRU based decoder with 2d attention module.

Metric Value
GFlops 0.03
MParams 4.33

Inputs

  1. Name: decoder_input, shape: 1. Previous predicted letter.
  2. Name: features, shape: 1, 16, 1024. Encoded features.
  3. Name: hidden, shape: 1, 1, 1024. Current state of the decoder.

Outputs

  1. Name: decoder_hidden, shape: 1, 1, 1024. Current context state of the LSTM cell.
  2. Name: decoder_output, shape: 1, 66. Classification confidence scores in the [0, 1] range for every letter.

Use text-detection demo

Model is supported by text-detection c++ demo. In order to use this model in the demo, user should pass the following options:

-tr_pt_first
-m_tr_ss "?0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
-tr_o_blb_nm "decoder_output"
-tr_composite
-dt simple -lower

For more information, please, see documentation of the demo.

Demo usage

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

Legal Information

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