Segmentation of merged characters by neural networks and shortest path
Identifieur interne : 002E76 ( Main/Exploration ); précédent : 002E75; suivant : 002E77Segmentation of merged characters by neural networks and shortest path
Auteurs : JIN WANG [États-Unis] ; J. JeanSource :
- Pattern recognition [ 0031-3203 ] ; 1994.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
A major problem with a neural network-based approach to printed character recognition is the segmentation of merged characters. A hybrid method is proposed which combines a neural network-based deferred segmentation scheme with conventional immediate segmentation techniques. In the deferred segmentation, a neural network is employed to distinguish single characters from composites. To find a proper vertical cut that separates a composite, a shortest-path algorithm seeking minimal-penalty curved cuts is used. Integrating those components with a multiresolution neural network OCR and an efficient spelling checker, the resulting system significantly improves its ability to read omnifont document text
Affiliations:
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Le document en format XML
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