Description from [https://github.com/brendenlake/omniglot][1] Omniglot data set for one-shot learning This dataset contains 1623 different handwritten characters from 50 different alphabets. Each of the 1623 characters was drawn online via Amazon's Mechanical Turk by 20 different people. Citing this data set Please cite the following paper: [Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction.](http://www.sciencemag.org/content/350/6266/1332.short) _Science_, 350(6266), 1332-1338. We are grateful for the [Omniglot](http://www.omniglot.com/) encyclopedia of writing systems for helping to make this data set possible, and for [Jason Gross](https://people.csail.mit.edu/jgross/) who was essential to the development and collection of this data set. PYTHON Python 2.7.* Requires scipy and numpy Key data files (images only): images_background.zip images_evaluation.zip images_background_small1.zip images_background_small2.zip To compare with the one-shot classification results in our paper, enter the 'one-shot-classification' directory and unzip 'all_runs.zip' and place all the folders 'run01',...,'run20' in the current directory. Run 'demo_classification.py' to demo a baseline model using Modified Hausdorff Distance. [1]: https://github.com/brendenlake/omniglot