Paper Info
Title
Dealing with Precise and Imprecise Decisions with a Dempster-Shafer Theory Based Algorithm in the Context of Handwritten Word Recognition
Abstract
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.
Year
DOI
Venue
2010
10.1109/ICFHR.2010.64
ICFHR
Keywords
Field
DocType
arabic handwritten isolated word,different length list,handwritten word recognition,handwriting recognition,classification process,single result,dempster-shafer theory,context model,decision process,imprecise decisions,generic evaluation methodology,classical accuracy rate,accuracy,context modeling,dempster shafer theory,databases
Arabic,Pattern recognition,Computer science,Word recognition,Handwriting recognition,Algorithm,Context model,Speech recognition,Natural language processing,Artificial intelligence,Decision process,Dempster–Shafer theory
Conference
Citations 
PageRank 
References 
0
0.34
15
Authors
3
Name
Order
Citations
PageRank
Thomas Burger1385.81
Yousri Kessentini210015.39
Thierry Paquet356556.65