Human or Machine?

This repository allows you to build computational models that can tell human and machine-generated handwriting apart.

Datasets

You can train the models from scratch with the following datasets:

• $1-GDS (5280 unistroke gestures, 16 classes): human data and synthetic data.

• $N-MMG (9600 unistroke and multistroke gestures, 16 classes): human data and synthetic data.

• Chars74k (3410 unistroke and multistroke gestures, 62 classes): human data and synthetic data.

You can try other datasets as long as you follow the expected CSV format.

Model training

This is how we trained our GRU classifier over the $1-GDS dataset:

~$ python3 main.py --human_dir csv-1dollar-human --synth_dir csv-1dollar-synth-best \
  --model_type gru --epochs 400 --patience 40 --batch_size 32 --activation tanh

When this process ends, a directory will be created (in /tmp by default) with the log history and the trained model file in h5 format.

There are many CLI options you might want to specify, such as --verbose 1 (to see more output info) or --out_dir somedir (to set the output directory).

To see all the available CLI options, run python3 main.py -h.

Model evaluation

If you already trained a model, you can evaluate it this way:

~$ python3 main.py --human_dir csv-1dollar-human --synth_dir csv-1dollar-synth-best \
  --eval_model path/to/model.h5 --model_type gru

Again, run python3 main.py -h to see all the available CLI options.

Paper

A preprint of our ICPR paper is publicly available: https://arxiv.org/abs/2010.13231

Citation

Please cite us using the following reference:

• L. A. Leiva, M. Diaz, M. A. Ferrer, R. Plamondon. Human or Machine? It Is Not What You Write, But How You Write It. Proc. ICPR, 2020.

@InProceedings{Leiva20_biometrics,
  author    = {Luis A. Leiva and Moises Diaz and Miguel A. Ferrer and Réjean Plamondon},
  title     = {Human or Machine? It Is Not What You Write, But How You Write It},
  booktitle = {Proc. ICPR},
  year      = {2020},
}