51 lines
2.0 KiB
ReStructuredText
51 lines
2.0 KiB
ReStructuredText
.. _digits_dataset:
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Optical recognition of handwritten digits dataset
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**Data Set Characteristics:**
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:Number of Instances: 1797
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:Number of Attributes: 64
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:Attribute Information: 8x8 image of integer pixels in the range 0..16.
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:Missing Attribute Values: None
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:Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)
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:Date: July; 1998
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This is a copy of the test set of the UCI ML hand-written digits datasets
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https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits
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The data set contains images of hand-written digits: 10 classes where
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each class refers to a digit.
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Preprocessing programs made available by NIST were used to extract
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normalized bitmaps of handwritten digits from a preprinted form. From a
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total of 43 people, 30 contributed to the training set and different 13
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to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of
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4x4 and the number of on pixels are counted in each block. This generates
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an input matrix of 8x8 where each element is an integer in the range
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0..16. This reduces dimensionality and gives invariance to small
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distortions.
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For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.
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T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.
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L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,
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1994.
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**References**
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- C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their
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Applications to Handwritten Digit Recognition, MSc Thesis, Institute of
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Graduate Studies in Science and Engineering, Bogazici University.
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- E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.
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- Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.
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Linear dimensionalityreduction using relevance weighted LDA. School of
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Electrical and Electronic Engineering Nanyang Technological University.
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2005.
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- Claudio Gentile. A New Approximate Maximal Margin Classification
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Algorithm. NIPS. 2000.
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|details-end|
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