Word-Graph and Character-Lattice Combination for KWS in Handwritten Documents

We present a handwritten text Keyword Spotting (KWS) approach based on the combination of KWS methods using word-graphs (WGs) and character-lattices (CLs). It aims to solve the problem that WG-based models present for out of vocabulary (OOV) keywords: since there is no available information about them in the lexicon or the language model, null scores are assigned. OOV keywords may have a significant impact on the global performance of KWS systems, as we show. By using a CL approach, which does not suffer from the previous problem, to estimate the OOV scores, we take advantage of both models, using the speed and accuracy that WGs provide for in-vocabulary keywords and the flexibility of the CL approach. This combination improves significantly both average precision and mean average precision over the two methods.