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 channelsH
- image heightW
- image width
Outputs#
Name:
decoder_hidden
, shape:1, 1, 1024
. Initial context state of the GRU cell.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#
Name:
decoder_input
, shape:1
. Previous predicted letter.Name:
features
, shape:1, 16, 1024
. Encoded features.Name:
hidden
, shape:1, 1, 1024
. Current state of the decoder.
Outputs#
Name:
decoder_hidden
, shape:1, 1, 1024
. Current context state of the LSTM cell.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.