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Use of a Confusion Network to Detect and Correct Errors in an On-Line Handwritten Sentence Recognition System

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2 Author(s)
Quiniou, S. ; Campus de Beaulieu, Rennes ; Anquetil, E.

In this paper we investigate the integration of a confusion network into an on-line handwritten sentence recognition system. The word posterior probabilities from the confusion network are used as confidence scored to detect potential errors in the output sentence from the Maximum A Posteriori decoding on a word graph. Dedicated classifiers (here, SVMs) are then trained to correct these errors and combine the word posterior probabilities with other sources of knowledge. A rejection phase is also introduced in the detection process. Experiments on handwritten sentences show a 28.5 % relative reduction of the word error rate.

Published in:

Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on  (Volume:1 )

Date of Conference:

23-26 Sept. 2007

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