Decision fusion for postal address recognition using belief functions

Abstract

Combining the outputs from several postal address readers (PARs) is a promising approach for improving the performances of mailing address recognition systems. In this paper, this problem is solved using the Transferable Belief Model, an uncertain reasoning framework based on Dempster-Shafer belief functions. Applying this framework to postal address recognition implies defining the frame of discernment (or set of possible answers to the problem under study), converting PAR outputs into belief functions (taking into account additional information such as confidence scores when available), combining the resulting belief functions, and making decisions. All these steps are detailed in this paper. Experimental results demonstrate the effectiveness of this approach as compared to simple combination rules.

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Maximal confidence intervals of the interval-valued belief structure and applications

“…It has been applied in a wide range of areas including information fusion [7,74,82,96], expert systems [1,13,18,75,92], pattern recognition and classification [15][16][17][23][24][25]30,48,70], diagnosis and reasoning [10,21,[36][37][38]51,52,54,55,69], knowledge reduction [83][84][85], decision analysis [8,[11][12][13][14]46,74,80,86,88,90,[93][94][95], intelligent control [33], process monitoring [71][72][73], audit risk assessment where / is an empty set, A is any subset of X, and 2 X is the power set of X consisting of all the subs...…”