Optical Recognition of Handwritten Digits

Donated on 6/30/1998

Two versions of this database available; see folder

Dataset Characteristics

Multivariate

Subject Area

Computer Science

Associated Tasks

Classification

Feature Type

Integer

# Instances

5620

# Features

64

Dataset Information

Additional Information

We used preprocessing programs made available by NIST to extract normalized bitmaps of handwritten digits from a preprinted form. From a total of 43 people, 30 contributed to the training set and different 13 to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of 4x4 and the number of on pixels are counted in each block. This generates an input matrix of 8x8 where each element is an integer in the range 0..16. This reduces dimensionality and gives invariance to small distortions. For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G. T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C. L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469, 1994.

Has Missing Values?

No

Introductory Paper

Methods of Combining Multiple Classifiers and Their Applications to Handwritten Digit Recognition

By C. Kaynak. 1995

Published in MSc Thesis, Institute of Graduate Studies in Science and Engineering, Bogazici University

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
Attribute1FeatureIntegerno
Attribute2FeatureIntegerno
Attribute3FeatureIntegerno
Attribute4FeatureIntegerno
Attribute5FeatureIntegerno
Attribute6FeatureIntegerno
Attribute7FeatureIntegerno
Attribute8FeatureIntegerno
Attribute9FeatureIntegerno
Attribute10FeatureIntegerno

0 to 10 of 65

Additional Variable Information

All input attributes are integers in the range 0..16. The last attribute is the class code 0..9

Class Labels

Class: No of examples in training set 0: 376 1: 389 2: 380 3: 389 4: 387 5: 376 6: 377 7: 387 8: 380 9: 382 Class: No of examples in testing set 0: 178 1: 182 2: 177 3: 183 4: 181 5: 182 6: 181 7: 179 8: 174 9: 180

Baseline Model Performance

Papers Citing this Dataset

Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms

By Erich Schubert, Peter Rousseeuw. 2018

Published in ArXiv.

Shuffle based Anomaly Detection in Multi-state System

By Dongdong Hou, Yang Cong, Gan Sun, Xiaowei Xu. 2017

Published in 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER).

Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

By Marcello Benedetti, John Realpe-Gómez, Rupak Biswas, Alejandro Perdomo-Ortiz. 2017

Published in Physical Review X.

Graph-based Composite Local Bregman Divergences on Discrete Sample Spaces

By Takafumi Kanamori, Takashi Takenouchi. 2016

Published in Neural networks : the official journal of the International Neural Network Society.

0 to 5 of 10

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10 citations
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Keywords

object recognitionimage processing

Creators

E. Alpaydin

C. Kaynak

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