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OHRS-MEWA: On-Line Handwriting Recognition System with Multi-environment Writer Adaptation

The writer adaptation arisen with the appearance and the excessive use of Handheld devices. These devices are conceived to be used in diverse user settings which can be stationary or mobile. Most of the works tackle the writer adaptation in the "sitting at a desk" environment, nevertheless we notice a lack of contributions in the multi-environment context. In this paper we present a multi-environment writer adaptation technique to improve accuracy of writer-independent recognition system. Our system is based on adaptation module (AM) which can greatly decrease error accuracy without changing the writer-independent system. The (AM) is built using IGAAM which is an incremental learning algorithm. First, we test the performance of the IGA-AM on Laviola dataset against GA-AM algorithm for writer adaptation. Second, we test the recognition accuracy by taking into account the writing style change proportionally to environment changes. Thus the system contains as much adaptation module as handled environments. In this paper we consider two stationary environments that are sitting at a desk and standing. Finally, results on multi environment dataset (REGIM-MEnv) are presented.