An adaptation module with growing and adjustment RBFNN using a Long-Term Memory

In this paper we proposed a writer adaptation system based on an adaptation module that is a plug-in for any writer-independent handwriting recognition systems. The adaptation module is a radial basis function neural network (RBF-NN) that is built using an incremental learning algorithm named GALTM-AM algorithm (Growing-Adjustment with Long-Term Memory). GALTM-AM train a new given data with some LTM data to suppress the interference. Therefore, we design two procedures to manage the LTM data. The first is produce and store. The second is retrieve and learn. This new learning algorithm is evaluated by the adaptation of a writer-independent handwriting recognition system. Moreover, the results using a benchmark database named LaViola prove the efficiency of the proposed GALTM-AM. Performance comparison of GALTM-AM algorithm over the existing approaches is presented.