{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/emnist-an-extension-of-mnist-to-handwritten","title":"EMNIST: an extension of MNIST to handwritten letters","arxiv_id":"1702.05373","date":"2017-02-17","proceeding":null,"authors":["Gregory Cohen","Saeed Afshar","Jonathan Tapson","André van Schaik"],"abstract":"The MNIST dataset has become a standard benchmark for learning,\nclassification and computer vision systems. Contributing to its widespread\nadoption are the understandable and intuitive nature of the task, its\nrelatively small size and storage requirements and the accessibility and\nease-of-use of the database itself. The MNIST database was derived from a\nlarger dataset known as the NIST Special Database 19 which contains digits,\nuppercase and lowercase handwritten letters. This paper introduces a variant of\nthe full NIST dataset, which we have called Extended MNIST (EMNIST), which\nfollows the same conversion paradigm used to create the MNIST dataset. The\nresult is a set of datasets that constitute a more challenging classification\ntasks involving letters and digits, and that shares the same image structure\nand parameters as the original MNIST task, allowing for direct compatibility\nwith all existing classifiers and systems. Benchmark results are presented\nalong with a validation of the conversion process through the comparison of the\nclassification results on converted NIST digits and the MNIST digits.","url_abs":"http://arxiv.org/abs/1702.05373v2","url_pdf":"http://arxiv.org/pdf/1702.05373v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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