A new smoothing method for lexicon-based handwritten text keyword spotting

Abstract

Lexicon-based handwritten text keyword spotting (KWS) has proven to be a very fast and accurate alternative to lexicon-free methods. Nevertheless, since lexicon-based KWS methods rely on a predefined vocabulary, fixed in the training phase, they perform poorly for any query keyword that was not included in it (i.e. out-of-vocabulary keywords). This turns the KWS system useless for that particular type of queries. In this paper, we present a new way of smoothing the scores of OOV keywords, and we compare it with previously published alternatives on different data sets.