# Dataset Card for digits dataset
Optical recognition of handwritten digits dataset

## Dataset Description

- **Homepage:** https://scikit-learn.org/stable/datasets/toy_dataset.html#digits-dataset

## Note - How to load this dataset directly with the datasets library
```
from datasets import load_dataset
dataset = load_dataset("sklearn-docs/digits",header=None)
```

### Dataset Summary

This is a copy of the test set of the UCI ML hand-written digits datasets https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits

The data set contains images of hand-written digits: 10 classes where each class refers to a digit.

Preprocessing programs made available by NIST were used 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.

### Data Instances

Number of Instances:
1797

Number of Attributes:
64

Attribute Information:
8x8 image of integer pixels in the range 0..16.

Missing Attribute Values:
None

Creator:
5. Alpaydin (alpaydin ‘@’ boun.edu.tr)

Date:
July; 1998


### Citation Information

References

C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their Applications to Handwritten Digit Recognition, MSc Thesis, Institute of Graduate Studies in Science and Engineering, Bogazici University.

Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.

Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. Linear dimensionalityreduction using relevance weighted LDA. School of Electrical and Electronic Engineering Nanyang Technological University. 2005.

Claudio Gentile. A New Approximate Maximal Margin Classification Algorithm. NIPS. 2000.


size_categories:1K<n<10K license:cc0-1.0 region:us
{"license": "cc0-1.0", "size_categories": ["1K<n<10K"]}
TAGS
#size_categories-1K<n<10K #license-cc0-1.0 #region-us 

# Dataset Card for digits dataset
Optical recognition of handwritten digits dataset

## Dataset Description

- Homepage: URL

## Note - How to load this dataset directly with the datasets library


### Dataset Summary

This is a copy of the test set of the UCI ML hand-written digits datasets URL

The data set contains images of hand-written digits: 10 classes where each class refers to a digit.

Preprocessing programs made available by NIST were used 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.

### Data Instances

Number of Instances:
1797

Number of Attributes:
64

Attribute Information:
8x8 image of integer pixels in the range 0..16.

Missing Attribute Values:
None

Creator:
5. Alpaydin (alpaydin ‘@’ URL)

Date:
July; 1998




References

C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their Applications to Handwritten Digit Recognition, MSc Thesis, Institute of Graduate Studies in Science and Engineering, Bogazici University.

Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.

Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. Linear dimensionalityreduction using relevance weighted LDA. School of Electrical and Electronic Engineering Nanyang Technological University. 2005.

Claudio Gentile. A New Approximate Maximal Margin Classification Algorithm. NIPS. 2000.


# Dataset Card for digits dataset
Optical recognition of handwritten digits dataset ## Dataset Description

- Homepage: URL ## Note - How to load this dataset directly with the datasets library ### Dataset Summary

This is a copy of the test set of the UCI ML hand-written digits datasets URL

The data set contains images of hand-written digits: 10 classes where each class refers to a digit.

Preprocessing programs made available by NIST were used 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. ### Data Instances

Number of Instances:
1797

Number of Attributes:
64

Attribute Information:
8x8 image of integer pixels in the range 0..16.

Missing Attribute Values:
None

Creator:
5. Alpaydin (alpaydin ‘@’ URL)

Date:
July; 1998




References

C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their Applications to Handwritten Digit Recognition, MSc Thesis, Institute of Graduate Studies in Science and Engineering, Bogazici University.

Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.

Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. Linear dimensionalityreduction using relevance weighted LDA. School of Electrical and Electronic Engineering Nanyang Technological University. 2005.

Claudio Gentile. A New Approximate Maximal Margin Classification Algorithm. NIPS. 2000.
TAGS
#size_categories-1K<n<10K #license-cc0-1.0 #region-us 
 # Dataset Card for digits dataset
Optical recognition of handwritten digits dataset ## Dataset Description

- Homepage: URL ## Note - How to load this dataset directly with the datasets library ### Dataset Summary

This is a copy of the test set of the UCI ML hand-written digits datasets URL

The data set contains images of hand-written digits: 10 classes where each class refers to a digit.

Preprocessing programs made available by NIST were used 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. ### Data Instances

Number of Instances:
1797

Number of Attributes:
64

Attribute Information:
8x8 image of integer pixels in the range 0..16.

Missing Attribute Values:
None

Creator:
5. Alpaydin (alpaydin ‘@’ URL)

Date:
July; 1998




References

C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their Applications to Handwritten Digit Recognition, MSc Thesis, Institute of Graduate Studies in Science and Engineering, Bogazici University.

Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.

Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. Linear dimensionalityreduction using relevance weighted LDA. School of Electrical and Electronic Engineering Nanyang Technological University. 2005.

Claudio Gentile. A New Approximate Maximal Margin Classification Algorithm. NIPS. 2000.
passage: TAGS
#size_categories-1K<n<10K #license-cc0-1.0 #region-us 
# Dataset Card for digits dataset
Optical recognition of handwritten digits dataset## Dataset Description

- Homepage: URL## Note - How to load this dataset directly with the datasets library### Dataset Summary

This is a copy of the test set of the UCI ML hand-written digits datasets URL

The data set contains images of hand-written digits: 10 classes where each class refers to a digit.

Preprocessing programs made available by NIST were used 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.