Dempster-Shafer based rejection strategy for handwritten word recognition
In this paper, a novel rejection strategy is proposed to optimize the reliability of a 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.
Thomas Burger Yousri Kessentini Thierry Paquet
Université de Bretagne-Sud, CNRS, Lab-STICC F-56017 Vannes cedex, France Université de Rouen, Laboratoire LITIS EA 4108 Site du Madrillet, St Etienne du Rouvray, France
国际会议
北京
英文
528-532
2011-09-01（万方平台首次上网日期，不代表论文的发表时间）