The objective is to identify each of a large number of black-and-white rectangular pixel displays as one of the 26 capital letters in the English alphabet. The character images were based on 20 different fonts, and each letter within these 20 fonts was randomly distorted to produce a file of 20,000 unique stimuli. Each stimulus was converted into 16 primitive numerical attributes (statistical moments and edge counts) which were then scaled to fit into a range of integer values from 0 through 15.
The Letter Recognition data set is available free of charge on the UCI Machine Learning Repository website [1]. See [2] for more details.
- Number of instances: 20,000
- Number of attributes: 17 (letter category and 16 numeric features)
- Attribute characteristics: Integer
- Associated Tasks: Classification
- Missing Attribute Values: None
[1] Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
[2] Frey, P. W., & Slate, D. J. (1991). Letter recognition using Holland-style adaptive classifiers.Machine learning,6(2), 161-182.