gaoxinjian-ustc / straug

Image transformations designed for Scene Text Recognition (STR) data augmentation. Published at ICCV 2021 Workshop on Interactive Labeling and Data Augmentation for Vision.

Data Augmentation for Scene Text Recognition (ICCV 2021 Workshop)

(Pronounced as "strog")

Paper

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.

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 = {IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)},
  year={2021},
  pubstate={published},
  tppubtype={inproceedings}
}