Title: Letter Image Recognition Data

Relevant Information:

   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.  We
   typically train on the first 16000 items and then use the resulting model
   to predict the letter category for the remaining 4000.

Number of Instances: 15997 train items

Number of Attributes: 17 (Letter category and 16 numeric features)

Attribute Information:
         1.     lettr   capital letter  (integers 1-26 stand for A-Z)
         2.     x-box   horizontal position of box      (integer)
         3.     y-box   vertical position of box        (integer)
         4.     width   width of box                    (integer)
         5.     high    height of box                   (integer)
         6.     onpix   total # on pixels               (integer)
         7.     x-bar   mean x of on pixels in box      (integer)
         8.     y-bar   mean y of on pixels in box      (integer)
         9.     x2bar   mean x variance                 (integer)
        10.     y2bar   mean y variance                 (integer)
        11.     xybar   mean x y correlation            (integer)
        12.     x2ybr   mean of x * x * y               (integer)
        13.     xy2br   mean of x * y * y               (integer)
        14.     x-ege   mean edge count left to right   (integer)
        15.     xegvy   correlation of x-ege with y     (integer)
        16.     y-ege   mean edge count bottom to top   (integer)
        17.     yegvx   correlation of y-ege with x     (integer)