text-detection-0003

Use Case and High-Level Description

Text detector based on FCOS architecture with MobileNetV2-like as a backbone for indoor/outdoor scenes with more or less horizontal text.

The key benefit of this model compared to the base model is its smaller size and faster performance.

Example

Specification

Metric Value
F-measure (harmonic mean of precision and recall on ICDAR2013) 88.45%
GFlops 7.78
MParams 2.26
Source framework PyTorch*

Performance

Inputs

  1. Name: input, shape: [1x3x704x704] - An input image in the format [1xCxHxW], where:

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

    Expected color order - BGR.

Outputs

  1. The boxes is a blob with shape: [N, 5], where N is the number of detected bounding boxes. For each detection, the description has the format: [x_min, y_min, x_max, y_max, conf], where:
    • (x_min, y_min) - coordinates of the top left bounding box corner
    • (x_max, y_max) - coordinates of the bottom right bounding box corner.
    • conf - confidence for the predicted class
  2. The labels is a blob with shape: [N], where N is the number of detected bounding boxes. In case of text detection, it is equal to 0 for each detected box.

Legal Information

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