EMNIST: an extension of MNIST to handwritten letters
This provides a new benchmark dataset for machine learning and computer vision researchers, enabling testing on more complex tasks without modifying existing classifiers, though it is incremental as it extends an established dataset.
The paper introduces EMNIST, an extension of the MNIST dataset to include handwritten letters, creating more challenging classification tasks while maintaining compatibility with existing systems. Benchmark results validate the conversion process by comparing classification performance on converted NIST digits and original MNIST digits.
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.