Abstract: Since varied training data sources are not profitable for the learning of optimal model parameters, then a novel text information extraction algorithm based on hidden Markov model with multiple templates is proposed, which makes use of the information of format and list separators to segment text, and then extracts text information through combining theparameters of releasing probability for universal training, using multiple form templates to train the parameters of initial probability and transition probability for hidden Markov mode. Experimental results show better performance in precision and recall over simple hidden Markov model.

Key words: text block , multiple templates, hidden markov model, text information extraction