Confidence Measures for an Address Reading System

In this paper the performance of different confidence measures used for an address recognition system are evaluated. The recognition system for cursive handwritten German address words is based on Hidden Markov Models (HMMs). It is essential, that the structure of the address (name, street, city, country) is known, so that a specific small but complete dictionary can be selected. Choosing a wrong dictionary (OOV: out-of-vocabulary) or misrecognize the word, the recognition result should be rejected by means of the confidence measure. This paper points out two aspects: the comparison of four confidence measures for single words -- based on the likelihood, a garbage-model, a two-best recognition or a character decoding -- and the comparison of using complete or wrong dictionaries. It is shown, that the best confidence measure -- the two-best distance -- has a quite different behavior using OOV