  The MNIST dataset has become a standard benchmark for learning,
classification and computer vision systems. Contributing to its widespread
adoption are the understandable and intuitive nature of the task, its
relatively small size and storage requirements and the accessibility and
ease-of-use of the database itself. The MNIST database was derived from a
larger dataset known as the NIST Special Database 19 which contains digits,
uppercase and lowercase handwritten letters. This paper introduces a variant of
the full NIST dataset, which we have called Extended MNIST (EMNIST), which
follows the same conversion paradigm used to create the MNIST dataset. The
result is a set of datasets that constitute a more challenging classification
tasks involving letters and digits, and that shares the same image structure
and parameters as the original MNIST task, allowing for direct compatibility
with all existing classifiers and systems. Benchmark results are presented
along with a validation of the conversion process through the comparison of the
classification results on converted NIST digits and the MNIST digits.

Computer Vision and Pattern Recognition