Integration Of N-Gram Language Models In Multiple Classifier Systems For Offline Handwritten Text Line Recognition
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE(2008)
摘要
Current multiple classifier systems for unconstrained handwritten text recognition do not provide a straightforward way to utilize language model information. In this paper, we describe a generic method to integrate a statistical n-gram language model into the combination of multiple offline handwritten text line recognizers. The proposed method first builds a word transition network and then rescores this network with an n-gram language model. Experimental evaluation conducted on a large dataset of offline handwritten text lines shows that the proposed approach improves the recognition accuracy over a reference system as well as over the original combination method that does not include a language model.
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关键词
Offline handwritten text line recognition, multiple classifier systems, language modeling
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