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In this paper we introduce a new layer for the task of handwriting recognition. We add semantic information by means of ontologies. The task of our recognizer therefore is not only to recognize the ASCII transcription of the handwritten document, but also to identify the semantic concepts which appear in the text. This task is called ontology-based information extraction (OBIE), which has been applied to electronic documents recently. OBIE methods first segment the text into tokens, then identify their values and their corresponding instances of the ontology, and finally try to generate new facts based on the text. To the authors' knowledge, in this paper OBIE is proposed for the first time in handwriting literature. In our experiments we have evaluated the process up to the instantiation. We have found that using not only the top alternative, but also the k-best alternatives increases the performance of information extraction. Furthermore, the use of an ontology-based lexicon results in another performance increase.