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, 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.

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, 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.

Download a Model and Convert it into OpenVINO™ IR Format

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

An example of using the Model Downloader:

omz_downloader --name <model_name>

An example of using the Model Converter:

omz_converter --name <model_name>

Demo usage

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