Dempster-Shafer Based Rejection Strategy for Handwritten Word Recognition

In this paper, a novel rejection strategy is proposed to optimize the reliability of an handwritten word recognition system. The proposed approach is based on several steps. First, we combine the outputs of several HMM classifiers using the Dempster-Shafer theory (DST). Then, we take advantage of the expressivity of mass functions (the counter part of probability distributions in DST) to characterize the quality/reliability of the classification. Finally, we use this characterization to decide whether a test word is rejected or not. Experiments carried out on RIMES and IFN/ENIT datasets show that the proposed approach outperforms other state-of-the-art rejection methods.