其目标是将大量黑白矩形像素显示器中的每一个识别为英文字母中的26个大写字母之一。字符图像基于20种不同的字体，并且这20种字体中的每个字母随机失真以产生20,000个独特刺激的文件。每个刺激被转换成16个基本的数字属性（统计矩和边缘计数），然后将其缩放以适合从0到15的整数值范围。我们通常在前16000个项目上进行训练，然后使用结果模型预测剩余的4000个字母类别。请参阅上面引用的文章以获取更多详细信息。(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.)