High performance Query-by-Example keyword spotting using Query-by-String techniques

Keyword Spotting (KWS) has been traditionally considered under two distinct frameworks: Query-by-Example (QbE) and Query-by-String (QbS). In both cases the user of the system wished to find occurrences of a particular keyword in a collection of document images. The difference is that, in QbE, the keyword is given as an exemplar image while, in QbS the keyword is given as a text string. In several works, the QbS scenario has been approached using QbE techniques; but the converse has not been studied in depth yet, despite of the fact that QbS systems typically achieve higher accuracy. In the present work, we present a very effective probabilistic approach to QbE KWS, based on highly accurate QbS KWS techniques which rely on models which need to be trained from annotated data. To assess the effectiveness of this approach, we tackle the segmentation-free QbE task of the ICFHR-2014 Competition on Handwritten KWS. Our approach achieves a mean average precision (mAP) as high as 0.715, which improves by more than 70% the best mAP achieved in this competition (0.419 under the same experimental conditions).