Handwritten character segmentation using transformation-based learning

Presents a character segmentation algorithm for unconstrained cursive handwritten text. The transformation-based learning method and a simplified variation of it are used in order to extract automatically rules that detect the segment boundaries. Comparative experimental results are given for a coll...

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Published inProceedings 15th International Conference on Pattern Recognition. ICPR-2000 Vol. 2; pp. 634 - 637 vol.2
Main Authors Kavallieratou, E., Stamatatos, E., Fakotakis, N., Kokkinakis, G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2000
Subjects
Character recognition
Data mining
Handwriting recognition
Histograms
Image segmentation
Learning systems
Optical character recognition software
Text recognition
Training data
Vocabulary
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Summary:Presents a character segmentation algorithm for unconstrained cursive handwritten text. The transformation-based learning method and a simplified variation of it are used in order to extract automatically rules that detect the segment boundaries. Comparative experimental results are given for a collection of multiwriter handwritten words. The achieved accuracy in detecting segment boundaries exceeds 82%. Moreover limited training data can provide very satisfactory results.
ISBN:9780769507507
0769507506
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2000.906155