In spite of the rapid advances in computers and communication technologies, a number of large-scale applications continue to rely heavily on the use of paper as the dominant medium, either in an intra-organizational or an inter-organizational environment. One major example of this category of paper intensive applications is the check processing application. In a number of countries, the value of each check is read by human eyes before the check is physically transported, in stages, from the point it was presented to the location of the branch of the bank which issued the blank check to the concerned account holder. This process of manual reading of each check involves significant time and cost. In this paper, a new approach is proposed to read the numerical amount field on the check; this field is also called the courtesy amount field. In the case of check processing, the segmentation of unconstrained strings into individual digits is a challenging task because one needs to accommodate special cases involving: connected or overlapping digits, broken digits, and digits physically connected to a piece of stroke that belongs to a neighboring digit. The system described in this paper involves three stages: the segmentation of the string into a series of individual characters; the normalization of each isolated character; and the recognition of each character based on a neural network classifier.