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Development of handprinting character recognition system using two stage shape and stroke classification

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Date Issued:
1988
Summary:
This thesis deals with the recognition of digitized handprinting characters. Digitized character images are thresholded, binarized and converted into 32 x 32 matrices. The binarized character matrices are preprocessed to remove noise and thin down to one pixel per linewidth. For dominant features, namely, (1) number of loops, (2) number of end-pixels, (3) number of 3-branch-pixels, and (4) number of 4-branch-pixels, are used as criteria to pre-classify characters into 14 groups. Characters belonging to larger groups are encoded into chain code and compiled into a data base. Recognition of characters belonging to larger groups is achieved by data base look-up and or decision tree tests if ambiguities occur in the data base entries. Recognition of characters belonging to the smaller groups is doned by decision tree tests.
Title: Development of handprinting character recognition system using two stage shape and stroke classification.
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Name(s): Tse, Hing Wing.
Florida Atlantic University, Degree grantor
Sudhakar, Raghavan, Thesis advisor
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 1988
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 229 p.
Language(s): English
Summary: This thesis deals with the recognition of digitized handprinting characters. Digitized character images are thresholded, binarized and converted into 32 x 32 matrices. The binarized character matrices are preprocessed to remove noise and thin down to one pixel per linewidth. For dominant features, namely, (1) number of loops, (2) number of end-pixels, (3) number of 3-branch-pixels, and (4) number of 4-branch-pixels, are used as criteria to pre-classify characters into 14 groups. Characters belonging to larger groups are encoded into chain code and compiled into a data base. Recognition of characters belonging to larger groups is achieved by data base look-up and or decision tree tests if ambiguities occur in the data base entries. Recognition of characters belonging to the smaller groups is doned by decision tree tests.
Identifier: 14486 (digitool), FADT14486 (IID), fau:11284 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (M.S.E.)--Florida Atlantic University, 1988.
Subject(s): Optical character recognition devices
Pattern recognition systems
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/14486
Sublocation: Digital Library
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.