We combine two techniques to improve the language modeling component of a Handwritten Text Recognition (HTR) system. On the one hand, we apply a previously developed 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. On the other hand, we apply rescoring methods to enable more complex language modeling in HTR. It is shown that these techniques complement each other very well, and that the combination leads to a significant error reduction in a practical HTR task for historical data.
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