emnist

The EMNIST dataset is a set of handwritten character digits derived from the NIST Special Database 19 and converted to a 28x28 pixel image format and dataset structure that directly matches the MNIST dataset.

• URL: https://www.nist.gov/node/1298471/emnist-dataset
• DatasetBuilder: tfds.image.mnist.EMNIST

emnist is configured with tfds.image.mnist.EMNISTConfig and has the following configurations predefined (defaults to the first one):

• byclass (v1.0.1) (Size: 535.73 MiB): EMNIST ByClass

• bymerge (v1.0.1) (Size: 535.73 MiB): EMNIST ByMerge

• balanced (v1.0.1) (Size: 535.73 MiB): EMNIST Balanced

• letters (v1.0.1) (Size: 535.73 MiB): EMNIST Letters

• digits (v1.0.1) (Size: 535.73 MiB): EMNIST Digits

• mnist (v1.0.1) (Size: 535.73 MiB): EMNIST MNIST

emnist/byclass

FeaturesDict({
    'image': Image(shape=(28, 28, 1), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=62),
})

emnist/bymerge

FeaturesDict({
    'image': Image(shape=(28, 28, 1), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=47),
})

emnist/balanced

FeaturesDict({
    'image': Image(shape=(28, 28, 1), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=47),
})

emnist/letters

FeaturesDict({
    'image': Image(shape=(28, 28, 1), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=37),
})

emnist/digits

FeaturesDict({
    'image': Image(shape=(28, 28, 1), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),
})

emnist/mnist

FeaturesDict({
    'image': Image(shape=(28, 28, 1), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),
})

Statistics

Urls

• https://www.nist.gov/node/1298471/emnist-dataset

Supervised keys (for as_supervised=True)

(u'image', u'label')

Citation

@article{cohen_afshar_tapson_schaik_2017,
    title={EMNIST: Extending MNIST to handwritten letters},
    DOI={10.1109/ijcnn.2017.7966217},
    journal={2017 International Joint Conference on Neural Networks (IJCNN)},
    author={Cohen, Gregory and Afshar, Saeed and Tapson, Jonathan and Schaik, Andre Van},
    year={2017}
}