A Comparison of Simultaneous Confidence Intervals to Identify Handwritten Digits

This paper evaluates the use of several known simultaneous confidence interval methods for the automated recognition of handwritten digits from data in a well-known handwriting database. Contained in this database are handwritten digits, 0 through 9, that were obtained from 42,000 participants' writing samples. The objective of the analyses is to utilize statistical testing procedures that can be easily automated by a computer to recognize which digit was written by a subject. The methodologies discussed in this paper are designed to be sensitive to Type I errors and will control an overall measure of these errors, called the Familywise Error Rate. The procedures were constructed based off of a training portion of the data set, then applied and validated on the remaining testing portion of the data.

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