Step 1:

Download backgrounds and put the light and dark backgrounds separately. We'll be using them for creating synthetic dataset. We have uploaded sample backgrounds in light_backgrounds and dark_backgrounds for reference.

Step 2:

Download fonts from here. These fonts will be used for randomly selected font-type while creating synthetic dataset.

Step 3:

Create synthetic data using ImageMagick. We have given an intuition behind creating synthetic data, in our blog. This can be done with following command:

python3 generate-images.py

The script first generates two directories light_background_crops and dark_background_crops containing 32x32 backgrounds crops. It then adds text and other artifacts like blur/noise/distortion to the backgrounds. To regenerate all data, delete light_background_crops and dark_background_crops. To generate training images, open the script and set OUTPUT_DIR = 'train/' and NUM_IMAGES_PER_CLASS = 800. Similarly, to generate test images, set OUTPUT_DIR = 'test/' and NUM_IMAGES_PER_CLASS = 200.

Step 4:

Training the model on the given dataset. A modified LeNet structure has been used to train our model, using Keras. This can be done with following command:

python3 train_model.py

Step 5:

In order to predict the digit or character in an image, execute the following command. Give the test image path as the argument.

python3 make_predictions.py <image_path>

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