ctpn

Use Case and High-Level Description

Detecting Text in Natural Image with Connectionist Text Proposal Network. For details see paper.

Specification

Metric Value
Type Object detection
GFlops 55.813
MParams 17.237
Source framework TensorFlow*

Accuracy

Metric Value
hmean 73.67%

Input

Original Model

Image, name: image_tensor, shape: 1, 600, 600, 3, format: B, H, W, C, where:

  • B - batch size
  • H - image height
  • W - image width
  • C - number of channels

Expected color order: BGR. Mean values: [102.9801, 115.9465, 122.7717].

Converted Model

Image, name: Placeholder, shape: 1, 3, 600, 600, format: B, C, H, W, where:

  • B - batch size
  • C - number of channels
  • H - image height
  • W - image width

Expected color order: BGR.

Output

Original Model

  1. Detection boxes, name: rpn_bbox_pred/Reshape_1, contains predicted regions, in format B, H, W, A, where:
    • B - batch size
    • H - image height
    • W - image width
    • A - vector of 4*N coordinates, where N is the number of detected anchors.
  2. Probability, name: Reshape_2, contains probabilities for predicted regions in a [0,1] range in format B, H, W, A, where:
    • B - batch size
    • H - image height
    • W - image width
    • A - vector of 4*N coordinates, where N is the number of detected anchors.

Converted Model

  1. Detection boxes, name: rpn_bbox_pred/Reshape_1/Transpose, shape: 1, 40, 18, 18 contains predicted regions, format: B, A, H, W, where:
    • B - batch size
    • A - vector of 4*N coordinates, where N is the number of detected anchors.
    • H - image height
    • W - image width
  2. Probability, name: Reshape_2/Transpose, shape: 1, 20, 18, 18, contains probabilities for predicted regions in a[0,1] range in format B, A, H, W, where:
    • B - batch size
    • A - vector of 2*N class probabilities (0 class for background, 1 class for text), where N is the number of detected anchors.
    • H - image height
    • W - image width

Download a Model and Convert it into Inference Engine Format

You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --name <model_name>

Legal Information

The original model is distributed under the following license:

MIT License
Copyright (c) 2017 shaohui ruan
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.