Data Augmentation for Scene Text Recognition

(Pronounced as "strog")

Paper

• ICCV 2021 Workshop
• Arxiv

Why it matters?

Scene Text Recognition (STR) requires data augmentation functions that are different from object recognition. STRAug is data augmentation designed for STR. It offers 36 data augmentation functions that are sorted into 8 groups. Each function supports 3 levels or magnitudes of severity or intensity.

Given a source image:

it can be transformed as follows:

1. warp.py - to generate Curve, Distort, Stretch (or Elastic) deformations

Curve | Distort | Stretch ------------ | ------------- | ------------- | |

2. geometry.py - to generate Perspective, Rotation, Shrink deformations

Perspective | Rotation | Shrink ------------ | ------------- | ------------- | |

3. pattern.py - to create different grids: Grid, VGrid, HGrid, RectGrid, EllipseGrid

Grid| VGrid| HGrid | RectGrid | EllipseGrid ------------ | ------------- | ------------- | ------------- | ------------- | | | |

4. blur.py - to generate synthetic blur: GaussianBlur, DefocusBlur, MotionBlur, GlassBlur, ZoomBlur

GaussianBlur | DefocusBlur | MotionBlur | GlassBlur | ZoomBlur ------------ | ------------- | ------------- | ------------- | ------------- | | | |

5. noise.py - to add noise: GaussianNoise, ShotNoise, ImpulseNoise, SpeckleNoise

GaussianNoise | ShotNoise | ImpulseNoise | SpeckleNoise ------------ | ------------- | ------------- | ------------- | | |

6. weather.py - to simulate certain weather conditions: Fog, Snow, Frost, Rain, Shadow

Fog | Snow | Frost | Rain | Shadow ------------ | ------------- | ------------- | ------------- | ------------- | | | |

7. camera.py - to simulate camera sensor tuning and image compression/resizing: Contrast, Brightness, JpegCompression, Pixelate

Contrast | Brightness | JpegCompression | Pixelate ------------ | ------------- | ------------- | ------------- | | |

8. process.py - all other image processing issues: Posterize, Solarize, Invert, Equalize, AutoContrast, Sharpness, Color

Posterize | Solarize | Invert | Equalize ------------ | ------------- | ------------- | ------------- | | |

AutoContrast | Sharpness | Color ------------ | ------------- | ------------- | |

Pip install

pip3 install straug

How to use

Command line (e.g. input image is nokia.png):

>>> from straug.warp import Curve
>>> from PIL import Image
>>> img = Image.open("nokia.png")
>>> img = Curve()(img, mag=3)
>>> img.save("curved_nokia.png")

Python script (see test.py):

python3 test.py --image=<target image>

For example:

python3 test.py --image=images/telekom.png

The corrupted images are in results directory.

If you want to randomly apply only the desired augmentation types among multiple augmentations, see test_random_aug.py

Reference

• Image corruptions (eg blur, noise, camera effects, fog, frost, etc) are based on the work of Hendrycks et al.

Citation

If you find this work useful, please cite:

@inproceedings{atienza2021data,
  title={Data Augmentation for Scene Text Recognition},
  author={Atienza, Rowel},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={1561--1570},
  year={2021}
}