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
By C. Kaynak. 1995
Published in MSc Thesis, Institute of Graduate Studies in Science and Engineering, Bogazici University
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| Attribute1 | Feature | Integer | no | ||
| Attribute2 | Feature | Integer | no | ||
| Attribute3 | Feature | Integer | no | ||
| Attribute4 | Feature | Integer | no | ||
| Attribute5 | Feature | Integer | no | ||
| Attribute6 | Feature | Integer | no | ||
| Attribute7 | Feature | Integer | no | ||
| Attribute8 | Feature | Integer | no | ||
| Attribute9 | Feature | Integer | no | ||
| Attribute10 | Feature | Integer | no |
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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
By Chong Peng, Qiang Cheng. 2019
Published in ArXiv.
By Erich Schubert, Peter Rousseeuw. 2018
Published in ArXiv.
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).
By Marcello Benedetti, John Realpe-Gómez, Rupak Biswas, Alejandro Perdomo-Ortiz. 2017
Published in Physical Review X.
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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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset optical_recognition_of_handwritten_digits = fetch_ucirepo(id=80) # data (as pandas dataframes) X = optical_recognition_of_handwritten_digits.data.features y = optical_recognition_of_handwritten_digits.data.targets # metadata print(optical_recognition_of_handwritten_digits.metadata) # variable information print(optical_recognition_of_handwritten_digits.variables)
Alpaydin,E. and Kaynak,C.. (1998). Optical Recognition of Handwritten Digits. UCI Machine Learning Repository. https://doi.org/10.24432/C50P49.
@misc{misc_optical_recognition_of_handwritten_digits_80,
author = {Alpaydin,E. and Kaynak,C.},
title = {{Optical Recognition of Handwritten Digits}},
year = {1998},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C50P49}
}Keywords
Creators
E. Alpaydin
C. Kaynak
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.