Context-aware lattice based filler approach for key word spotting in handwritten documents

The so-called filler or garbage Hidden Markov Models (HMM-Filler) are among the most widely used models for lexicon-free, query by string key word spotting (KWS) in the fields of speech recognition and (lately) handwritten text recognition. However, it has important drawbacks. First, the keyword-specific HMM Viterbi decoding process needed to obtain the confidence scores of each spotted word involves a large computational cost. Second, in its traditional conception, the model does not take into account any context information - and more recent works where simple character bi-gram context is used show that not only the computational cost becomes even larger, but also the required keyword-specific language model becomes quite intricate to build. In a previous work we introduced KWS methods based on character lattices which proved very much simpler and faster than the traditional HMM-Filler, while providing practically identical results. Here we extend our previous work by using context-aware character lattices obtained by means of Viterbi decoding with high-order character N-gram models. Experimental results show that, as compared with a direct 2-gram HMM-filler implementation, the proposed approach requires between one and two orders of magnitude less query computing time. Moreover, for the first time in the field of handwritten text KWS, Filler-based results for N-grams up to N = 6 are reported, clearly showing a great impact of context on precision-recall performance.