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Incorporating Conditional Independence Assumption with Support Vector Machines to Enhance Handwritten Character Segmentation Per

9 years 11 months ago
Incorporating Conditional Independence Assumption with Support Vector Machines to Enhance Handwritten Character Segmentation Per
Learning Bayesian Belief Networks (BBN) from corpora and incorporating the extracted inferring knowledge with a Support Vector Machines (SVM) classifier has been applied to character segmentation for unconstrained handwritten text. By taking advantage of the plethora in unlabeled data found in image databases in addition to some available labeled examples, we overcome the expensive task of annotating the whole set of training data and the performance of the character segmentation learner is increased. Apart from this approach, which has not previously used for this task, we have experimented with two well-known machine learning methods (Learning Vector Quantization and a simplified version of the Transformation-Based Learning theory). We argue that a classifier generated from BBN and SVM is well suited for learning to identify the correct segment boundaries. Empirical results will support this claim. Performance has been methodically evaluated using both English and Modern Greek corpo...
Manolis Maragoudakis, Ergina Kavallieratou, Nikos
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2002
Where ICPR
Authors Manolis Maragoudakis, Ergina Kavallieratou, Nikos Fakotakis
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