(Pronounced as "strog")

• ICCV 2021 Workshop
• Arxiv

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

pip3 install straug

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

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

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}
}