Realization of an offline system for the recognition of the handwritten numeric chains

In this study we propose an off line system for the recognition of the handwritten numeric chains. Firstly, we realized a recognition system of the isolated handwritten digits, in this part, the study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm and fed by several parameter vectors; the objective of this operation is to determine the parameter vector who gives the best performances. The used parameters to form the parameter vectors are extracted on the binary images of the digits by several methods: the distribution sequence, sondes application, the Barr features, The centered moments of image coding according to the Freeman directions, and the centered moments of the different projections and profiles. Secondly, the study is extended for the reading of the handwritten numeric chains constituted of a variable number of digits. Vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits).