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EMNIST: an extension of MNIST to handwritten letters

Western Sydney University
Cohen, Gregory ; Afshar, Saeed ; Tapson, Jonathan ; van Schaik, Andre
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.26183/m9k1-zr06&rft.title=EMNIST: an extension of MNIST to handwritten letters&rft.identifier=https://doi.org/10.26183/na5y-xs64&rft.publisher=Western Sydney University&rft.description=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. A Read Me file describing the database is included in the available attachments.Note: The available zip files are each > 500MB in size. Should these files become unavailable from the website provided, please contact Western Sydney University Library about this record.  &rft.creator=Cohen, Gregory &rft.creator=Afshar, Saeed &rft.creator=Tapson, Jonathan &rft.creator=van Schaik, Andre &rft.date=2017&rft.edition=undefined&rft.relation=https://doi.org/10.1109/IJCNN.2017.7966217&rft.relation=https://arxiv.org/abs/1702.05373v2&rft_rights=Copyright Western Sydney University&rft_subject=Computer vision&rft_subject=Learning (artificial intelligence)&rft_subject=Biometrics&rft_subject=Pattern recognition&rft.type=dataset&rft.language=English ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.26183/m9k1-zr06&rft.title=EMNIST: an extension of MNIST to handwritten letters&rft.identifier=https://doi.org/10.26183/na5y-xs64&rft.publisher=Western Sydney University&rft.description=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. A Read Me file describing the database is included in the available attachments.Note: The available zip files are each > 500MB in size. Should these files become unavailable from the website provided, please contact Western Sydney University Library about this record.  &rft.creator=Cohen, Gregory &rft.creator=Afshar, Saeed &rft.creator=Tapson, Jonathan &rft.creator=van Schaik, Andre &rft.date=2017&rft.edition=undefined&rft.relation=https://doi.org/10.1109/IJCNN.2017.7966217&rft.relation=https://arxiv.org/abs/1702.05373v2&rft_rights=Copyright Western Sydney University&rft_subject=Computer vision&rft_subject=Learning (artificial intelligence)&rft_subject=Biometrics&rft_subject=Pattern recognition&rft.type=dataset&rft.language=English Access the data

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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.

A Read Me file describing the database is included in the available attachments.
Note: The available zip files are each > 500MB in size. Should these files become unavailable from the website provided, please contact Western Sydney University Library about this record.

 

Created: 2017-05-01

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