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Data Augmentation for Scene Text Recognition

(Pronounced as "strog")

Paper

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
  1. geometry.py - to generate Perspective, Rotation, Shrink deformations
Perspective Rotation Shrink
  1. pattern.py - to create different grids: Grid, VGrid, HGrid, RectGrid, EllipseGrid
Grid VGrid HGrid RectGrid EllipseGrid
  1. blur.py - to generate synthetic blur: GaussianBlur, DefocusBlur, MotionBlur, GlassBlur, ZoomBlur
GaussianBlur DefocusBlur MotionBlur GlassBlur ZoomBlur
  1. noise.py - to add noise: GaussianNoise, ShotNoise, ImpulseNoise, SpeckleNoise
GaussianNoise ShotNoise ImpulseNoise SpeckleNoise
  1. weather.py - to simulate certain weather conditions: Fog, Snow, Frost, Rain, Shadow
Fog Snow Frost Rain Shadow
  1. camera.py - to simulate camera sensor tuning and image compression/resizing: Contrast, Brightness, JpegCompression, Pixelate
Contrast Brightness JpegCompression Pixelate
  1. 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={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={1561--1570},
  year={2021}
}