Dealing with Precise and Imprecise Decisions with a Dempster-Shafer Theory Based Algorithm in the Context of Handwritten Word Recognition

The classification process in handwriting recognition is designed to provide lists of results rather than single results, so that context models can be used as post-processing. Most of the time, the length of the list is determined once and for all the items to classify. Here, we present a method based on Dempster-Shafer theory that allows a different length list for each item, depending on the precision of the information involved in the decision process. As it is difficult to compare the results of such an algorithm to classical accuracy rates, we also propose a generic evaluation methodology. Finally, this algorithm is evaluated on Latin and Arabic handwritten isolated word datasets.