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Discrimination of machine-printed from handwritten text using simple structural characteristics

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2 Author(s)

In this paper, we present a trainable approach to discriminate between machine-printed and handwritten text. An integrated system able to localize text areas and split them in text-lines is used. A set of simple and easy-to-compute structural characteristics that capture the differences between machine-printed and handwritten text-lines is introduced. Experiments on document images taken from IAM-DB and GRUHD databases show a remarkable performance of the proposed approach that requires minimal training data.

Published in:

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on  (Volume:1 )

Date of Conference:

23-26 Aug. 2004