Processing Bank Checks with Genetic Programming and Histograms

In spite of evolution of electronic techniques, a large number of applications continue to rely on the use of paper as the dominant medium. Bank checks are a widely known example. When filled by hand, the processing of the written information requires either a human or a special software which has intelligent abilities. This paper examines the issue of reading the amount of money written on the checks. Genetic Programming (GP) technique is used for dealing with this problem. A new type of input representation is proposed: histograms. Several numerical experiments with GP are performed by using large datasets taken from the MNIST benchmarking set. Preliminary results show a good behavior of the method.