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OPTICAL CHARACTER RECOGNITION: A STATISTICAL MODEL OF MULTI-ENGINE OPTICAL CHARACTER RECOGNITION SYSTEMS

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Date Issued:
2004
Abstract/Description:
This thesis is a benchmark performed on three commercial Optical Character Recognition (OCR) engines. The purpose of this benchmark is to characterize the performance of the OCR engines with emphasis on the correlation of errors between each engine. The benchmarks are performed for the evaluation of the effect of a multi-OCR system employing a voting scheme to increase overall recognition accuracy. This is desirable since currently OCR systems are still unable to recognize characters with 100% accuracy. The existing error rates of OCR engines pose a major problem for applications where a single error can possibly effect significant outcomes, such as in legal applications. The results obtained from this benchmark are the primary determining factor in the decision of implementing a voting scheme. The experiment performed displayed a very high accuracy rate for each of these commercial OCR engines. The average accuracy rate found for each engine was near 99.5% based on a less than 6,000 word document. While these error rates are very low, the goal is 100% accuracy in legal applications. Based on the work in this thesis, it has been determined that a simple voting scheme will help to improve the accuracy rate.
Title: OPTICAL CHARACTER RECOGNITION: A STATISTICAL MODEL OF MULTI-ENGINE OPTICAL CHARACTER RECOGNITION SYSTEMS.
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Name(s): McDonald, Mercedes Terre, Author
M Richie, Samuel, Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2004
Publisher: University of Central Florida
Language(s): English
Abstract/Description: This thesis is a benchmark performed on three commercial Optical Character Recognition (OCR) engines. The purpose of this benchmark is to characterize the performance of the OCR engines with emphasis on the correlation of errors between each engine. The benchmarks are performed for the evaluation of the effect of a multi-OCR system employing a voting scheme to increase overall recognition accuracy. This is desirable since currently OCR systems are still unable to recognize characters with 100% accuracy. The existing error rates of OCR engines pose a major problem for applications where a single error can possibly effect significant outcomes, such as in legal applications. The results obtained from this benchmark are the primary determining factor in the decision of implementing a voting scheme. The experiment performed displayed a very high accuracy rate for each of these commercial OCR engines. The average accuracy rate found for each engine was near 99.5% based on a less than 6,000 word document. While these error rates are very low, the goal is 100% accuracy in legal applications. Based on the work in this thesis, it has been determined that a simple voting scheme will help to improve the accuracy rate.
Identifier: CFE0000123 (IID), ucf:46188 (fedora)
Note(s): 2004-08-01
M.S.E.E.
College of Engineering and Computer Science, Department of Electrical and Computer Engineering
This record was generated from author submitted information.
Subject(s): Optical Character Recognition (OCR)
Voting Scheme
Character Recognition Accuracy
Machine Readability
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0000123
Restrictions on Access: campus 2006-01-31
Host Institution: UCF

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