Rejection strategies for handwritten word recognition

In this paper, we investigate different rejection strategies to verify the output of a handwriting recognition system. We evaluate a variety of novel rejection thresholds including global, class-dependent and hypothesis-dependent thresholds to improve the reliability in recognizing unconstrained handwritten words. The rejection thresholds are applied in a post-processing mode to either reject or accept the output of the handwriting recognition system which consists of a list with the N-best word hypotheses. Experimental results show that the best rejection strategy is able to improve the reliability of the handwriting recognition system from about 78% to 94% while rejecting 30% of the word hypotheses.