A new view of the output from word recognition

Word recognition has changed in the past years. Implementations have become better, which allows larger vocabularies to be recognized; many of the implementations now available are suited well to specific tasks, but several have to be combined to obtain maximum benefit; and they are still hampered by deficiencies, for example when trying to decide among similar words. The new view of the output interface from word recognition presented here aims at various goals: easier combination of comparable word classification systems, better post-processing of word recognition results at higher levels (where more context is available), and improvement of out-of-vocabulary behavior. The proposed solution is to provide two different result scores instead of one single value: an overall quality measure, indicating the credibility of recognition, and a similarity measure for each word alternative. This shifts the responsibility for decisions from low-level word recognition tasks to higher levels, while retaining all necessary, recognition information. This, however, gives rise to the need to define new performance metrics for evaluation. Implementations of the proposed output interface for two recognition engines are sketched.