An Intelligent Sample Selection Approach to Language Model Adaptation for Hand-Written Text Recognition

We present an intelligent sample selection approach to language model adaptation for handwritten text recognition, which exploits a combination of in-domain and out-of-domain data for construction of language models. In comparison to approaches proposed in the literature, our approach is characterized by a careful consideration of the criteria used for ranking samples and an innovative approach to sample selection which iteratively extends the training set for two language models. We propose two methods, in which agreement or disagreement of two ranking criteria (one for each language model) guides the selection of samples to add to the training sets of the models. Both approaches are shown to clearly outperform a strong baseline consisting of a carefully tuned interpolation of in-domain and out-of-domain language models.