Selecting a restoration technique to minimize OCR error.
Identifieur interne : 000055 ( PubMed/Corpus ); précédent : 000054; suivant : 000056Selecting a restoration technique to minimize OCR error.
Auteurs : M. Cannon ; M. Fugate ; D R Hush ; C. ScovelSource :
- IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council [ 1045-9227 ] ; 2003.
Abstract
This paper introduces a learning problem related to the task of converting printed documents to ASCII text files. The goal of the learning procedure is to produce a function that maps documents to restoration techniques in such a way that on average the restored documents have minimum optical character recognition error. We derive a general form for the optimal function and use it to motivate the development of a nonparametric method based on nearest neighbors. We also develop a direct method of solution based on empirical error minimization for which we prove a finite sample bound on estimation error that is independent of distribution. We show that this empirical error minimization problem is an extension of the empirical optimization problem for traditional M-class classification with general loss function and prove computational hardness for this problem. We then derive a simple iterative algorithm called generalized multiclass ratchet (GMR) and prove that it produces an optimal function asymptotically (with probability 1). To obtain the GMR algorithm we introduce a new data map that extends Kesler's construction for the multiclass problem and then apply an algorithm called Ratchet to this mapped data, where Ratchet is a modification of the Pocket algorithm . Finally, we apply these methods to a collection of documents and report on the experimental results.
DOI: 10.1109/TNN.2003.811711
PubMed: 18238033
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pubmed:18238033Le document en format XML
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Scovel</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2003">2003</date><idno type="doi">10.1109/TNN.2003.811711</idno><idno type="RBID">pubmed:18238033</idno><idno type="pmid">18238033</idno><idno type="wicri:Area/PubMed/Corpus">000055</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Selecting a restoration technique to minimize OCR error.</title><author><name sortKey="Cannon, M" sort="Cannon, M" uniqKey="Cannon M" first="M" last="Cannon">M. Cannon</name><affiliation><nlm:affiliation>Comput. Res. and Applications Group, Los Alamos Nat. Lab., NM, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Fugate, M" sort="Fugate, M" uniqKey="Fugate M" first="M" last="Fugate">M. Fugate</name></author><author><name sortKey="Hush, D R" sort="Hush, D R" uniqKey="Hush D" first="D R" last="Hush">D R Hush</name></author><author><name sortKey="Scovel, C" sort="Scovel, C" uniqKey="Scovel C" first="C" last="Scovel">C. Scovel</name></author></analytic><series><title level="j">IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council</title><idno type="ISSN">1045-9227</idno><imprint><date when="2003" type="published">2003</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper introduces a learning problem related to the task of converting printed documents to ASCII text files. The goal of the learning procedure is to produce a function that maps documents to restoration techniques in such a way that on average the restored documents have minimum optical character recognition error. We derive a general form for the optimal function and use it to motivate the development of a nonparametric method based on nearest neighbors. We also develop a direct method of solution based on empirical error minimization for which we prove a finite sample bound on estimation error that is independent of distribution. We show that this empirical error minimization problem is an extension of the empirical optimization problem for traditional M-class classification with general loss function and prove computational hardness for this problem. We then derive a simple iterative algorithm called generalized multiclass ratchet (GMR) and prove that it produces an optimal function asymptotically (with probability 1). To obtain the GMR algorithm we introduce a new data map that extends Kesler's construction for the multiclass problem and then apply an algorithm called Ratchet to this mapped data, where Ratchet is a modification of the Pocket algorithm . Finally, we apply these methods to a collection of documents and report on the experimental results.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="PubMed-not-MEDLINE"><PMID Version="1">18238033</PMID><DateCreated><Year>2008</Year><Month>02</Month><Day>01</Day></DateCreated><DateCompleted><Year>2012</Year><Month>10</Month><Day>02</Day></DateCompleted><Article PubModel="Print"><Journal><ISSN IssnType="Print">1045-9227</ISSN><JournalIssue CitedMedium="Print"><Volume>14</Volume><Issue>3</Issue><PubDate><Year>2003</Year></PubDate></JournalIssue><Title>IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council</Title><ISOAbbreviation>IEEE Trans Neural Netw</ISOAbbreviation></Journal><ArticleTitle>Selecting a restoration technique to minimize OCR error.</ArticleTitle><Pagination><MedlinePgn>478-90</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1109/TNN.2003.811711</ELocationID><Abstract><AbstractText>This paper introduces a learning problem related to the task of converting printed documents to ASCII text files. The goal of the learning procedure is to produce a function that maps documents to restoration techniques in such a way that on average the restored documents have minimum optical character recognition error. We derive a general form for the optimal function and use it to motivate the development of a nonparametric method based on nearest neighbors. We also develop a direct method of solution based on empirical error minimization for which we prove a finite sample bound on estimation error that is independent of distribution. We show that this empirical error minimization problem is an extension of the empirical optimization problem for traditional M-class classification with general loss function and prove computational hardness for this problem. We then derive a simple iterative algorithm called generalized multiclass ratchet (GMR) and prove that it produces an optimal function asymptotically (with probability 1). To obtain the GMR algorithm we introduce a new data map that extends Kesler's construction for the multiclass problem and then apply an algorithm called Ratchet to this mapped data, where Ratchet is a modification of the Pocket algorithm . Finally, we apply these methods to a collection of documents and report on the experimental results.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Cannon</LastName><ForeName>M</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Comput. Res. and Applications Group, Los Alamos Nat. 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