Integration of an on-line handwriting recognition system in a smart phone device

We present the evolution of our academic development to a technology driven application: the integration of unconstraint cursive on-line handwritten characters into a smart phone device. The ultimate goal of this work is to implement an accurate handwriting recognizer into mobile devices with limited computing and memory resources. Hierarchical fuzzy modeling is used to obtain a compact and robust knowledge representation and the decision process is based on an adapted fuzzy inference system to reduce computing without decreasing the performance. We describe the basic architecture of the recognition system called "ResifCar", its practical adaptation to the mobile device constraints and the recognition rates both on cursive isolated letters (91.9%) and on isolated digits (92.3%) in a writer independent context based on 100 different writers.