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Abstract

A method for exploiting correlated mail streams using optical character recognition is provided in which a static database is used to store data based on training. Real-time data for the parameters of interest, such as address block location, zip code, city, state, and font size or type is collected from the mail processing equipment in order to generate a statistical information database. The dynamic database can include probability density functions, correlations statistics, mean, variance, and high order moments. The statistical parameters are tracked using recursive least squares schemes with various windowing options, as will as moving average linear filters. Based on cost models which indicate the cost of making various types of errors in the OCR process, a decision threshold is determined which is based on the real-time statistics of the mail stream. The decision threshold determines the confidence level required by the adaptive process in order to assign a previously rejected mail pieces based solely on correlation statistics. The decisions threshold will adapt to the statistics of the mail stream and is not a constant value. Previously unassigned characters are assigned according to the decision threshold determination and assignment processes.

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