semeion
OpenML dataset with id 1501
No author found.
Full work available at URL: https://api.openml.org/data/v1/download/1592293/semeion.arff
Upload date: 25 May 2015
Dataset Characteristics
Number of classes: 10
Number of features: 257 (numeric: 256, symbolic: 1 and in total binary: 0 )
Number of instances: 1,593
Number of instances with missing values: 0
Number of missing values: 0
Author: Semeion Research Center of Sciences of Communication Source: UCI Please cite: Semeion Research Center of Sciences of Communication, via Sersale 117, 00128 Rome, Italy Tattile Via Gaetano Donizetti, 1-3-5,25030 Mairano (Brescia), Italy.
Dataset Description
Semeion Handwritten Digit Data Set, where 1593 handwritten digits from around 80 persons were scanned and documented. The each of the 256 variables V1 - V256 describe one of the pixels and their corresponding values.
Sources
The dataset was created by Tactile Srl, Brescia, Italy (http://www.tattile.it) and donated in 1994 to Semeion Research Center of Sciences of Communication, Rome, Italy (http://www.semeion.it), for machine learning research.
For any questions, e-mail Massimo Buscema (m.buscema '@' semeion.it) or Stefano Terzi (s.terzi '@' semeion.it)
DataSet Information
A total of 1593 handwritten digits from around 80 persons were scanned, stretched in a rectangular box 16x16 in a gray scale of 256 values. Then each pixel of each image was scaled into a boolean (1/0) value using a fixed threshold.
Each person wrote in a paper all the digits from 0 to 9, twice. The commitment was to write the digit the first time in the normal way (trying to write each digit accurately) and the second time in a fast way (with no accuracy).
The best validation protocol for this dataset seems to be a 5x2CV, 50% Tune (Train +Test) and completely blind 50% Validation
Attribute Information
This dataset consists of 1593 records (rows) and 256 attributes (columns). Each record represents a handwritten digit, originally scanned with a resolution of 256 grays scale (28). Each pixel of the each original scanned image was first stretched, and after scaled between 0 and 1 (setting to 0 every pixel whose value was under the value 127 of the grey scale (127 included) and setting to 1 each pixel whose original value in the grey scale was over 127). Finally, each binary image was scaled again into a 16x16 square box (the final 256 binary attributes).
Relevant Papers
M Buscema, MetaNet: The Theory of Independent Judges, in Substance Use & Misuse 33(2)1998, pp 439-461.
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