Predictor of OCR accuracy using statistical techniques

Systems that predict optical character recognition (OCR) accuracy of an input image by a given OCR system were developed. Seven features associated with image defects were identified and utilized. Two kinds of nonparametric classification engines, the nearest neighbor rule-based and neural network-based, were implemented. The performance of these systems were compared to an old heuristic-based system using a cost model of a large-scale document conversion process and a test data set consisting of 502 pages. The results show that the performance of new classifiers were better than that of the heuristic-based system. The neural network-based system outperformed the nearest-neighbor-based system. These new systems can be used to reduce the cost of a large-scale document conversion process by discriminating good quality pages for OCR from degraded images for manual data entry.

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor dc:publisher

University of Nevada, Las Vegas

Year

1996

Author dc:creator

• Gonzalez, Juan Manuel

Statement dc:rights

• IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

Language dc:language

English

Identifier

https://oasis.library.unlv.edu/rtds/589

OAI identifier oai:identifier

oai:oasis.library.unlv.edu:rtds-1588