EMNIST: an extension of MNIST to handwritten letters

Abstract

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.

The dataset is now available for download from https://www.westernsydney.edu.au/bens/home/reproducible_research/emnist. This link is also included in the revised article

Cited by in corpus (6)

• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Unseen Class Discovery in Open-world Classification
• SEALion: a Framework for Neural Network Inference on Encrypted Data
• Learning Generative Models across Incomparable Spaces
• A New Loss Function for CNN Classifier Based on Pre-defined Evenly-Distributed Class Centroids
• AutoAssist: A Framework to Accelerate Training of Deep Neural Networks