Date of Publication

12-1998

Document Type

Master's Thesis

Degree Name

Master of Science in Computer Science

Subject Categories

Computer Sciences

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Adviser

Arnulfo P. Azcarraga

Defense Panel Chair

Maria Alvarez

Defense Panel Member

Elmer P. Dadios
Philip Chan

Abstract/Summary

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.

Abstract Format

html

Language

English

Format

Electronic

Accession Number

TG03109

Shelf Location

Archives, The Learning Commons, 12F Henry Sy Sr. Hall

Physical Description

96, [11] leaves ; 28 cm.

Keywords

Writing; Image processing; Character sets (Data processing); Neural networks (Computer science)

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