Papers › EMNIST: an extension of MNIST to handwritten letters
EMNIST: an extension of MNIST to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, André van Schaik
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.
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Code
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | EMNIST-Balanced | OPIUM Classifier | Accuracy | 78.94 | #18 of 20 | Archive leaderboard | report |
| Image Classification | EMNIST-Balanced | Linear Classifier | Accuracy | 50.93 | #20 of 20 | Archive leaderboard | report |
| Image Classification | EMNIST-Digits | OPIUM Classifier | Accuracy (%) | 96.30 | #6 of 7 | Archive leaderboard | report |
| Image Classification | EMNIST-Digits | Linear Classifier | Accuracy (%) | 84.70 | #7 of 7 | Archive leaderboard | report |
| Image Classification | EMNIST-Letters | OPIUM Classifier | Accuracy | 85.27 | #10 of 11 | Archive leaderboard | report |
| Image Classification | EMNIST-Letters | Linear Classifier | Accuracy | 55.78 | #11 of 11 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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