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Context-Aware Lattice based Filler approach for Key Word Spotting in Handwritten Documents. 13th International Conference on Document Analysis and Recognition, 2015. IEEE Computer Society.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 in the fields of speech recognition and (lately) handwritten text recognition. This approach 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 ``filler'' does not take into account any context information. And in case it does, even though the involved greater computational cost, the required keyword-specific language model building can become quite intricate. This paper presents novel keyword spotting results by using a character lattice based KWS approach with context information provided by employing high order N-gram models. This approach has proved to be faster than the traditional HMM-Filler approach, where required confidence scores are computed directly from character lattices produced during a single Viterbi decoding process using N-gram models. Experiments show that, as compared with the HMM-filler approach using 2-gram model, the character lattice based method requires between one and two orders of magnitude less query computing time.