Adaptive Fuzzy Inference System Plug-In For Writer Adaptation

In this paper we proposed a writer adaptation system based on an adaptive fuzzy inference system (AFIS) that can be plug-in for any writer-independent handwriting recognition systems. The AFIS starts with an empty rule set. Subsequently, a supervised incremental learning algorithm is operated. When the user reports a misclassification, rule are added or updated. The proposed learning algorithm is evaluated by the adaptation of a writer-independent recognition system (LipiTk). Moreover, the results using a benchmark database named LaViola prove the efficiency of the proposed system. The error rate reduction varies between 66.32% and 41.05%.