7. A method comprising: receiving, by a computer, an image of a handwritten item; performing, by said computer, a word segmentation process on said image to produce a sub-image; extracting, by said computer, a set of feature vectors from said sub-image; computing, by said computer, a first log-likelihood score of said feature vectors using a word model having a first structure comprising a Hidden Markov Model (HMM); computing, by said computer, a second log-likelihood score of said feature vectors using a background model having a second structure comprising a Gaussian Mixture Model (GMM); computing, by said computer, a final score for said sub-image by subtracting said second log-likelihood score from said first log-likelihood score; comparing, by said computer, said final score against a predetermined standard to produce a word identification result; and outputting, by said computer, said word identification result.
7. A method comprising: receiving, by a computer, an image of a handwritten item; performing, by said computer, a word segmentation process on said image to produce a sub-image; extracting, by said computer, a set of feature vectors from said sub-image; computing, by said computer, a first log-likelihood score of said feature vectors using a word model having a first structure comprising a Hidden Markov Model (HMM); computing, by said computer, a second log-likelihood score of said feature vectors using a background model having a second structure comprising a Gaussian Mixture Model (GMM); computing, by said computer, a final score for said sub-image by subtracting said second log-likelihood score from said first log-likelihood score; comparing, by said computer, said final score against a predetermined standard to produce a word identification result; and outputting, by said computer, said word identification result. 8. The method according to claim 7 , said first structure comprising a different number of states than said second structure.