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Fast HMM-Filler approach for Key Word Spotting in Handwritten Documents. Proceedings of the Twelfth International Conference on Document Analysis and Recognition (ICDAR), 2013. pp. 501-505. AThe so-called filler or garbage Hidden Markov Models (HMM) are among the most widely used models for lexicon-free, query by string key word spotting in the fields of speech recognition and (lately) handwritten text recognition. An important drawback of this approach is the large compu- tational cost of the keyword-specific HMM Viterbi decoding process needed to obtain the confidence scores of each word to be spotted. This paper presents a novel way to compute such confidence scores, directly from character lattices produced during a single Viterbi decoding process using only the “filler” model (i.e. no explicit keyword-specific decoding is needed). Experiments show that, as compared with the classical HMM- filler approach, the proposed method obtains essentially the same spotting results, while requiring between one and two orders of magnitude less query computing time.