EMNIST

E363691

dataset handwritten character dataset image dataset

EMNIST is an extended handwritten character dataset that builds on MNIST by including both digits and letters for more comprehensive character recognition tasks.

Try in SPARQL Jump to: Surface forms Disambiguation Statements Elicitation Referenced by

All labels observed (8)

Label	Occurrences
EMNIST canonical	1
EMNIST Balanced	1
EMNIST ByClass	1
EMNIST ByMerge	1
EMNIST Digits	1
EMNIST Letters	1
EMNIST MNIST	1
NIST handwriting databases	1

How this entity was disambiguated

This entity first appeared as the object of triple T3507217 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07

Target entity: EMNIST
Context triple: [MNIST, inspiredDataset, EMNIST]

A. MNIST
MNIST is a widely used benchmark dataset of handwritten digit images commonly employed for training and evaluating image classification algorithms in machine learning and computer vision.
B. CIFAR
CIFAR (the Canadian Institute for Advanced Research) is a Canadian global research organization that supports long-term, collaborative, interdisciplinary research, including major initiatives in artificial intelligence.
C. DIGIT
DIGIT is the European Commission’s Directorate‑General responsible for shaping, implementing, and managing the EU institutions’ digital, IT, and cybersecurity strategies and services.
D. Gradient-based learning applied to document recognition
"Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
E. NMTI
NMTI is a prestigious United States presidential award that honors individuals, teams, and companies for outstanding contributions to technological innovation and advancement.
F. None of above. chosen
G. Unsure - the case is ambiguous/there is not enough information to decide.

NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07

Target entity: EMNIST
Target entity description: EMNIST is an extended handwritten character dataset that builds on MNIST by including both digits and letters for more comprehensive character recognition tasks.

A. MNIST
MNIST is a widely used benchmark dataset of handwritten digit images commonly employed for training and evaluating image classification algorithms in machine learning and computer vision.
B. CIFAR
CIFAR (the Canadian Institute for Advanced Research) is a Canadian global research organization that supports long-term, collaborative, interdisciplinary research, including major initiatives in artificial intelligence.
C. DIGIT
DIGIT is the European Commission’s Directorate‑General responsible for shaping, implementing, and managing the EU institutions’ digital, IT, and cybersecurity strategies and services.
D. Gradient-based learning applied to document recognition
"Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
E. NMTI
NMTI is a prestigious United States presidential award that honors individuals, teams, and companies for outstanding contributions to technological innovation and advancement.
F. None of above. chosen

Statements (48)

Predicate	Object
instanceOf	dataset ⓘ handwritten character dataset ⓘ image dataset ⓘ
basedOn	MNIST ⓘ
compatibleWith	MNIST model architectures ⓘ convolutional neural networks ⓘ
dataType	image ⓘ label ⓘ
extends	MNIST ⓘ
hasCharacteristic	includes letters in addition to digits ⓘ more classes than MNIST ⓘ same image resolution as MNIST ⓘ
hasDomain	artificial intelligence ⓘ computer vision ⓘ pattern recognition ⓘ
hasFormat	grayscale images ⓘ
hasImageSize	28x28 pixels ⓘ
hasLabelType	alphanumeric characters ⓘ digits 0-9 ⓘ letters A-Z ⓘ letters a-z ⓘ
hasProperty	balanced splits ⓘ multiple subsets ⓘ
hasSubset	EMNIST self-linksurface differs ⓘ surface form: EMNIST Balanced EMNIST self-linksurface differs ⓘ surface form: EMNIST ByClass EMNIST self-linksurface differs ⓘ surface form: EMNIST ByMerge EMNIST self-linksurface differs ⓘ surface form: EMNIST Digits EMNIST self-linksurface differs ⓘ surface form: EMNIST Letters EMNIST self-linksurface differs ⓘ surface form: EMNIST MNIST
hasTask	image classification ⓘ multiclass classification ⓘ
includes	handwritten digits ⓘ handwritten letters ⓘ lowercase letters ⓘ uppercase letters ⓘ
languageIndependent	true ⓘ
relatedTo	MNIST ⓘ NIST Special Database 19 ⓘ
typicalUse	evaluating classification accuracy ⓘ training neural networks ⓘ
usedFor	benchmarking classification algorithms ⓘ deep learning research ⓘ handwritten character recognition ⓘ machine learning research ⓘ optical character recognition ⓘ
usedIn	academic research ⓘ educational examples ⓘ tutorials on deep learning ⓘ

How these facts were elicited

Referenced by (8)

Full triples — surface form annotated when it differs from this entity's canonical label.

MNIST → inspiredDataset → EMNIST ⓘ

NIST Special Database 1 → associatedWith → EMNIST ⓘ

this entity surface form: NIST handwriting databases