Date of Publication
Master of Science in Computer Science
College of Computer Studies
Arnulfo P. Azcarraga
Defense Panel Chair
Defense Panel Member
Elmer P. Dadios
Cursive script recognition is commonly based on finding letters within a word and recognizing them separately. The segmentation process is ambiguous and difficult. This paper presents a hybrid method which combines individual recognizers: segmentation-based and word-based, to cope with difficulties in recognizing cursive script. Words are first segmented into smaller subimages. A neural network is used to identify possible letters among the group. Letter information is combined with word shape information to get word identity. Recognition results of individual and hybrid recognizers are presented. The hybrid recognizer is found to perform better than individual recognizers.
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96,  leaves ; 28 cm.
Writing; Image processing; Character sets (Data processing); Neural networks (Computer science)
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Monreal, J. T. (1998). A hybrid approach for off-line cursive script recognition. Retrieved from https://animorepository.dlsu.edu.ph/etd_masteral/2495