Keywords
optical, character, recognition, OCR, simple, voting, multi-dimensional, indexing
Abstract
The three major optical character recognition (OCR) engines (ExperVision, Scansoft OCR, and Abby OCR) in use today are all capable of recognizing text at near perfect percentages. The remaining errors however have proven very difficult to identify within a single engine. Recent research has shown that a comparison between the errors of the three engines proved to have very little correlation, and thus, when used in conjunction, may be useful to increase accuracy of the final result. This document discusses the implementation and results of a simple voting system designed to prove the hypothesis and show a statistical improvement in overall accuracy. Additional aspects of implementing an improved OCR scheme such as dealing with multiple engine data output alignment and recognizing application specific solutions are also addressed in this research. Although voting systems are currently in use by many major OCR engine developers, this research focuses on the addition of a collaborative system which is able to utilize the various positive aspects of multiple engines while also addressing the immediate need for practical industry applications such as litigation and forms processing. Doculex TM, a major developer and leader in the document imaging industry, has provided the funding for this research.
Notes
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Graduation Date
2005
Semester
Spring
Advisor
Weeks, Arthur
Degree
Master of Science in Computer Engineering (M.S.Cp.E.)
College
College of Engineering and Computer Science
Department
Electrical and Computer Engineering
Degree Program
Computer Engineering
Format
application/pdf
Identifier
CFE0000380
URL
http://purl.fcla.edu/fcla/etd/CFE0000380
Language
English
Release Date
May 2005
Length of Campus-only Access
None
Access Status
Masters Thesis (Open Access)
STARS Citation
Sprague, Christopher, "Autonomous Repair Of Optical Character Recognition Data Through Simple Voting And Multi-dimensional Indexing Techniques" (2005). Electronic Theses and Dissertations. 397.
https://stars.library.ucf.edu/etd/397