*********** Ocr.nns is a NeuNet Pro Sample File *********** This file contains special properties that allow NeuNet Pro to recognize it as authorized NeuNet sample data. Anyone using the unlicensed version of NeuNet Pro is welcome to experiment with this sample data. Please do not modify this file, or it will lose its status as authorized sample data. For further information about Neunet Pro and additional sample data, please visit the NeuNet Pro website at http://www.cormactech.com/neunet All of this data has been collected from publicly available sources. CorMac Technologies Inc. does not guarantee the accuracy of the data. This data is intended solely for experimental purposes. *************** More about the Ocr Data ******************* 1. Title: Letter Image Recognition Data 2. Source Information -- Creator: David J. Slate -- Odesta Corporation; 1890 Maple Ave; Suite 115; Evanston, IL 60201 -- Donor: David J. Slate (dave@math.nwu.edu) (708) 491-3867 -- Date: January, 1991 3. Past Usage: -- P. W. Frey and D. J. Slate (Machine Learning Vol 6 #2 March 91): "Letter Recognition Using Holland-style Adaptive Classifiers". The research for this article investigated the ability of several variations of Holland-style adaptive classifier systems to learn to correctly guess the letter categories associated with vectors of 16 integer attributes extracted from raster scan images of the letters. The best accuracy obtained was a little over 80%. It would be interesting to see how well other methods do with the same data. 4. 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. See the article cited above for more details. 5. Number of Instances: 20000 6. Number of Attributes: 17 (Letter category and 16 numeric features) 7. Attribute Information: 1. lettr capital letter (26 values from A to 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) 8. Missing Attribute Values: None 9. Class Distribution: 789 A 766 B 736 C 805 D 768 E 775 F 773 G 734 H 755 I 747 J 739 K 761 L 792 M 783 N 753 O 803 P 783 Q 758 R 748 S 796 T 813 U 764 V 752 W 787 X 786 Y 734 Z