Optical character recognition by a neural network
Identifieur interne : 000070 ( Istex/Corpus ); précédent : 000069; suivant : 000071Optical character recognition by a neural network
Auteurs : Michael Sabourin ; Amar MiticheSource :
- Neural Networks [ 0893-6080 ] ; 1992.
English descriptors
Abstract
An optical character recognition (OCR) system, which uses a multilayer perceptron (MLP) neural network classifier, is described. The neural network classifier has the advantage of being fast (highly parallel), easily trainable, and capable of creating arbitrary partitions of the input feature space. Issues in design of the neural network that we examine include the selection of input features, the choice of network learning and momentum parameters, and the selection of training patterns. We also provide a detailed analysis of the learning parameters to provide insight into the MLP, and to suggest a mechanism to automatically tune these parameters. An OCR neural network classifier was trained to recognize characters from a large number of fonts, thereby approaching an omnifont environment. Samples were selected from over 200 fonts and 50 typical office documents, for a total of 110,000 training patterns. In order to evaluate the performance of the MLP classifier, a comparison is made with a high performance dynamic contour warping (DCW) classifier. The base recognition rate on the test set is 96.7% for the neural network classifier, compared to 95.9% for the DCW classifier.
Url:
DOI: 10.1016/S0893-6080(05)80144-3
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<front><div type="abstract" xml:lang="en">An optical character recognition (OCR) system, which uses a multilayer perceptron (MLP) neural network classifier, is described. The neural network classifier has the advantage of being fast (highly parallel), easily trainable, and capable of creating arbitrary partitions of the input feature space. Issues in design of the neural network that we examine include the selection of input features, the choice of network learning and momentum parameters, and the selection of training patterns. We also provide a detailed analysis of the learning parameters to provide insight into the MLP, and to suggest a mechanism to automatically tune these parameters. An OCR neural network classifier was trained to recognize characters from a large number of fonts, thereby approaching an omnifont environment. Samples were selected from over 200 fonts and 50 typical office documents, for a total of 110,000 training patterns. In order to evaluate the performance of the MLP classifier, a comparison is made with a high performance dynamic contour warping (DCW) classifier. The base recognition rate on the test set is 96.7% for the neural network classifier, compared to 95.9% for the DCW classifier.</div>
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<abstract xml:lang="en"><p>An optical character recognition (OCR) system, which uses a multilayer perceptron (MLP) neural network classifier, is described. The neural network classifier has the advantage of being fast (highly parallel), easily trainable, and capable of creating arbitrary partitions of the input feature space. Issues in design of the neural network that we examine include the selection of input features, the choice of network learning and momentum parameters, and the selection of training patterns. We also provide a detailed analysis of the learning parameters to provide insight into the MLP, and to suggest a mechanism to automatically tune these parameters. An OCR neural network classifier was trained to recognize characters from a large number of fonts, thereby approaching an omnifont environment. Samples were selected from over 200 fonts and 50 typical office documents, for a total of 110,000 training patterns. In order to evaluate the performance of the MLP classifier, a comparison is made with a high performance dynamic contour warping (DCW) classifier. The base recognition rate on the test set is 96.7% for the neural network classifier, compared to 95.9% for the DCW classifier.</p>
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<ce:abstract-sec><ce:simple-para>An optical character recognition (OCR) system, which uses a multilayer perceptron (MLP) neural network classifier, is described. The neural network classifier has the advantage of being fast (highly parallel), easily trainable, and capable of creating arbitrary partitions of the input feature space. Issues in design of the neural network that we examine include the selection of input features, the choice of network learning and momentum parameters, and the selection of training patterns. We also provide a detailed analysis of the learning parameters to provide insight into the MLP, and to suggest a mechanism to automatically tune these parameters. An OCR neural network classifier was trained to recognize characters from a large number of fonts, thereby approaching an omnifont environment. Samples were selected from over 200 fonts and 50 typical office documents, for a total of 110,000 training patterns. In order to evaluate the performance of the MLP classifier, a comparison is made with a high performance dynamic contour warping (DCW) classifier. The base recognition rate on the test set is 96.7% for the neural network classifier, compared to 95.9% for the DCW classifier.</ce:simple-para>
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<tail><ce:bibliography><ce:section-title>References</ce:section-title>
<ce:bibliography-sec><ce:bib-reference id="bib1"><ce:label>Baird and Thompson, 1987</ce:label>
<sb:reference><sb:contribution langtype="en"><sb:authors><sb:author><ce:surname>Baird</ce:surname>
<ce:given-name>H.S.</ce:given-name>
</sb:author>
<sb:author><ce:surname>Thompson</ce:surname>
<ce:given-name>K.</ce:given-name>
</sb:author>
</sb:authors>
<sb:title><sb:maintitle>Reading chess</sb:maintitle>
</sb:title>
</sb:contribution>
<sb:host><sb:edited-book><sb:title><sb:maintitle>Proceedings of the IEEE Computer Society Workshop on Computer Vision</sb:maintitle>
</sb:title>
<sb:date>1987</sb:date>
</sb:edited-book>
<sb:pages><sb:first-page>277</sb:first-page>
<sb:last-page>282</sb:last-page>
</sb:pages>
</sb:host>
</sb:reference>
</ce:bib-reference>
<ce:bib-reference id="bib2"><ce:label>Cash and Hatamian, 1987</ce:label>
<sb:reference><sb:contribution langtype="en"><sb:authors><sb:author><ce:surname>Cash</ce:surname>
<ce:given-name>G.L.</ce:given-name>
</sb:author>
<sb:author><ce:surname>Hatamian</ce:surname>
<ce:given-name>M.</ce:given-name>
</sb:author>
</sb:authors>
<sb:title><sb:maintitle>Optical character recognition by the method of moments</sb:maintitle>
</sb:title>
</sb:contribution>
<sb:host><sb:issue><sb:series><sb:title><sb:maintitle>Computer Vision, Graphics and Image Processing</sb:maintitle>
</sb:title>
<sb:volume-nr>39</sb:volume-nr>
</sb:series>
<sb:date>1987</sb:date>
</sb:issue>
<sb:pages><sb:first-page>291</sb:first-page>
<sb:last-page>310</sb:last-page>
</sb:pages>
</sb:host>
</sb:reference>
</ce:bib-reference>
<ce:bib-reference id="bib3"><ce:label>Duffie, 1985</ce:label>
<sb:reference><sb:comment>unpubished M.Eng.</sb:comment>
<sb:contribution langtype="en"><sb:authors><sb:author><ce:surname>Duffie</ce:surname>
<ce:given-name>P.K.</ce:given-name>
</sb:author>
</sb:authors>
<sb:title><sb:maintitle>Contour elastic matching for omni-font character recognition</sb:maintitle>
</sb:title>
</sb:contribution>
<sb:host><sb:edited-book><sb:book-series><sb:series><sb:title><sb:maintitle>Thesis</sb:maintitle>
</sb:title>
</sb:series>
</sb:book-series>
<sb:date>1985</sb:date>
<sb:publisher><sb:name>McGill University</sb:name>
<sb:location>New York</sb:location>
</sb:publisher>
</sb:edited-book>
</sb:host>
</sb:reference>
</ce:bib-reference>
<ce:bib-reference id="bib4"><ce:label>Haykin, 1978</ce:label>
<sb:reference><sb:contribution langtype="en"><sb:authors><sb:author><ce:surname>Haykin</ce:surname>
<ce:given-name>S.</ce:given-name>
</sb:author>
</sb:authors>
<sb:title><sb:maintitle>Communication systems</sb:maintitle>
</sb:title>
</sb:contribution>
<sb:host><sb:book><sb:date>1978</sb:date>
<sb:publisher><sb:name>John Wiley & Sons</sb:name>
<sb:location>New York</sb:location>
</sb:publisher>
</sb:book>
</sb:host>
</sb:reference>
</ce:bib-reference>
<ce:bib-reference id="bib5"><ce:label>Kahan, et al, 1987</ce:label>
<sb:reference><sb:contribution langtype="en"><sb:authors><sb:author><ce:surname>Kahan</ce:surname>
<ce:given-name>S.</ce:given-name>
</sb:author>
<sb:author><ce:surname>Pavlidis</ce:surname>
<ce:given-name>T.</ce:given-name>
</sb:author>
<sb:author><ce:surname>Baird</ce:surname>
<ce:given-name>H.S.</ce:given-name>
</sb:author>
</sb:authors>
<sb:title><sb:maintitle>On the recognition of printed characters of any font and size</sb:maintitle>
</sb:title>
</sb:contribution>
<sb:host><sb:issue><sb:series><sb:title><sb:maintitle>IEEE Pattern Analysis and Machine Intelligence</sb:maintitle>
</sb:title>
<sb:volume-nr>9</sb:volume-nr>
</sb:series>
<sb:date>1987</sb:date>
</sb:issue>
<sb:pages><sb:first-page>274</sb:first-page>
<sb:last-page>287</sb:last-page>
</sb:pages>
</sb:host>
</sb:reference>
</ce:bib-reference>
<ce:bib-reference id="bib6"><ce:label>Kohonen, 1988</ce:label>
<sb:reference><sb:contribution langtype="en"><sb:authors><sb:author><ce:surname>Kohonen</ce:surname>
<ce:given-name>T.</ce:given-name>
</sb:author>
</sb:authors>
<sb:title><sb:maintitle>Self-organization and associative memory</sb:maintitle>
</sb:title>
</sb:contribution>
<sb:host><sb:edited-book><sb:edition>2nd ed.</sb:edition>
<sb:date>1988</sb:date>
<sb:publisher><sb:name>Springer-Verlag</sb:name>
<sb:location>Berlin</sb:location>
</sb:publisher>
</sb:edited-book>
</sb:host>
</sb:reference>
</ce:bib-reference>
<ce:bib-reference id="bib7"><ce:label>Lippman, 1987</ce:label>
<sb:reference><sb:contribution langtype="en"><sb:authors><sb:author><ce:surname>Lippman</ce:surname>
<ce:given-name>R.P.</ce:given-name>
</sb:author>
</sb:authors>
<sb:title><sb:maintitle>An introduction to computing with neural networks</sb:maintitle>
</sb:title>
</sb:contribution>
<sb:host><sb:edited-book><sb:book-series><sb:series><sb:title><sb:maintitle>IEEE Acoustics, Speech, and Signal Processing Magazine</sb:maintitle>
</sb:title>
</sb:series>
</sb:book-series>
<sb:date>1987</sb:date>
</sb:edited-book>
<sb:pages><sb:first-page>4</sb:first-page>
<sb:last-page>22</sb:last-page>
</sb:pages>
</sb:host>
</sb:reference>
</ce:bib-reference>
<ce:bib-reference id="bib8"><ce:label>Minsky and Papert, 1988</ce:label>
<sb:reference><sb:contribution langtype="en"><sb:authors><sb:author><ce:surname>Minsky</ce:surname>
<ce:given-name>M.L.</ce:given-name>
</sb:author>
<sb:author><ce:surname>Papert</ce:surname>
<ce:given-name>S.A.</ce:given-name>
</sb:author>
</sb:authors>
<sb:title><sb:maintitle>Perceptrons. An introduction to computational geometry</sb:maintitle>
</sb:title>
</sb:contribution>
<sb:host><sb:book><sb:date>1988</sb:date>
<sb:publisher><sb:name>MIT Press</sb:name>
<sb:location>Cambridge, MA</sb:location>
</sb:publisher>
</sb:book>
</sb:host>
</sb:reference>
</ce:bib-reference>
<ce:bib-reference id="bib9"><ce:label>Roberts and Agarwal, 1990</ce:label>
<sb:reference><sb:contribution langtype="en"><sb:authors><sb:author><ce:surname>Roberts</ce:surname>
<ce:given-name>G.</ce:given-name>
</sb:author>
<sb:author><ce:surname>Agarwal</ce:surname>
<ce:given-name>V.</ce:given-name>
</sb:author>
</sb:authors>
<sb:title><sb:maintitle>Course notes: VLSI methods for neural networks</sb:maintitle>
</sb:title>
</sb:contribution>
<sb:host><sb:book><sb:date>1990</sb:date>
<sb:publisher><sb:name>Department Electrical Engineering, McGill University</sb:name>
</sb:publisher>
</sb:book>
</sb:host>
</sb:reference>
</ce:bib-reference>
<ce:bib-reference id="bib10"><ce:label>Robillard, 1990</ce:label>
<sb:reference><sb:comment>unpublished Masters</sb:comment>
<sb:contribution langtype="iso" xml:lang="fr"><sb:authors><sb:author><ce:surname>Robillard</ce:surname>
<ce:given-name>S.</ce:given-name>
</sb:author>
</sb:authors>
<sb:title><sb:maintitle>Contribution à l'étude des mémoires associatives</sb:maintitle>
</sb:title>
</sb:contribution>
<sb:host><sb:edited-book><sb:book-series><sb:series><sb:title><sb:maintitle>Thesis</sb:maintitle>
</sb:title>
</sb:series>
</sb:book-series>
<sb:date>1990</sb:date>
<sb:publisher><sb:name>INRS-Telecommunications</sb:name>
<sb:location>Montreal, Canada</sb:location>
</sb:publisher>
</sb:edited-book>
</sb:host>
</sb:reference>
</ce:bib-reference>
<ce:bib-reference id="bib11"><ce:label>Rumelhart and McClelland, 1986</ce:label>
<sb:reference><sb:contribution langtype="en"><sb:authors><sb:author><ce:surname>Rumelhart</ce:surname>
<ce:given-name>D.E.</ce:given-name>
</sb:author>
<sb:author><ce:surname>McClelland</ce:surname>
<ce:given-name>J.L.</ce:given-name>
</sb:author>
</sb:authors>
</sb:contribution>
<sb:host><sb:edited-book><sb:book-series><sb:series><sb:title><sb:maintitle>Parallel distributed processing</sb:maintitle>
</sb:title>
<sb:volume-nr>vol I and II</sb:volume-nr>
</sb:series>
</sb:book-series>
<sb:date>1986</sb:date>
<sb:publisher><sb:name>MIT Press</sb:name>
<sb:location>Cambridge, MA</sb:location>
</sb:publisher>
</sb:edited-book>
</sb:host>
</sb:reference>
</ce:bib-reference>
<ce:bib-reference id="bib12"><ce:label>Sinha and Prasada, 1987</ce:label>
<sb:reference><sb:comment>May</sb:comment>
<sb:contribution langtype="en"><sb:authors><sb:author><ce:surname>Sinha</ce:surname>
<ce:given-name>R.M.K.</ce:given-name>
</sb:author>
<sb:author><ce:surname>Prasada</ce:surname>
<ce:given-name>B.</ce:given-name>
</sb:author>
</sb:authors>
<sb:title><sb:maintitle>An integrated omni-font text recognition system</sb:maintitle>
</sb:title>
</sb:contribution>
<sb:host><sb:issue><sb:series><sb:title><sb:maintitle>Rapport Tech. INRS-Telecommunications, No. 87-21</sb:maintitle>
</sb:title>
</sb:series>
<sb:date>1987</sb:date>
</sb:issue>
</sb:host>
</sb:reference>
</ce:bib-reference>
<ce:bib-reference id="bib13"><ce:label>Sinha and Prasada, 1988</ce:label>
<sb:reference><sb:contribution langtype="en"><sb:authors><sb:author><ce:surname>Sinha</ce:surname>
<ce:given-name>R.M.K.</ce:given-name>
</sb:author>
<sb:author><ce:surname>Prasada</ce:surname>
<ce:given-name>B.</ce:given-name>
</sb:author>
</sb:authors>
<sb:title><sb:maintitle>Visual text recognition through contextual processing</sb:maintitle>
</sb:title>
</sb:contribution>
<sb:host><sb:issue><sb:series><sb:title><sb:maintitle>Pattern Recognition</sb:maintitle>
</sb:title>
<sb:volume-nr>21</sb:volume-nr>
</sb:series>
<sb:date>1988</sb:date>
</sb:issue>
<sb:pages><sb:first-page>463</sb:first-page>
<sb:last-page>479</sb:last-page>
</sb:pages>
</sb:host>
</sb:reference>
</ce:bib-reference>
<ce:bib-reference id="bib14"><ce:label>Sinha,</ce:label>
<ce:other-ref><ce:textref>Sinha, R. M. K., Prasada, B., Houle, G., & Sabourin, M. (in press). Hybrid contextual text recognition with string matching. <ce:italic>IEEE Pattern Analysis and Machine Intelligence</ce:italic>
.</ce:textref>
</ce:other-ref>
</ce:bib-reference>
<ce:bib-reference id="bib15"><ce:label>Suen, et al, 1980</ce:label>
<sb:reference><sb:contribution langtype="en"><sb:authors><sb:author><ce:surname>Suen</ce:surname>
<ce:given-name>C.Y.</ce:given-name>
</sb:author>
<sb:author><ce:surname>Berthod</ce:surname>
<ce:given-name>M.</ce:given-name>
</sb:author>
<sb:author><ce:surname>Mori</ce:surname>
<ce:given-name>S.</ce:given-name>
</sb:author>
</sb:authors>
<sb:title><sb:maintitle>Automatic recognition of handprinted characters—The state of the art</sb:maintitle>
</sb:title>
</sb:contribution>
<sb:host><sb:edited-book><sb:book-series><sb:series><sb:title><sb:maintitle>Proceedings of the IEEE</sb:maintitle>
</sb:title>
<sb:volume-nr>68</sb:volume-nr>
</sb:series>
</sb:book-series>
<sb:date>1980</sb:date>
</sb:edited-book>
<sb:pages><sb:first-page>469</sb:first-page>
<sb:last-page>487</sb:last-page>
</sb:pages>
</sb:host>
</sb:reference>
</ce:bib-reference>
<ce:bib-reference id="bib16"><ce:label>Tappert, 1982</ce:label>
<sb:reference><sb:contribution langtype="en"><sb:authors><sb:author><ce:surname>Tappert</ce:surname>
<ce:given-name>C.C.</ce:given-name>
</sb:author>
</sb:authors>
<sb:title><sb:maintitle>Cursive script recognition by elastic matching</sb:maintitle>
</sb:title>
</sb:contribution>
<sb:host><sb:issue><sb:series><sb:title><sb:maintitle>IBM Journal of Research</sb:maintitle>
</sb:title>
<sb:volume-nr>10</sb:volume-nr>
</sb:series>
<sb:date>1982</sb:date>
</sb:issue>
<sb:pages><sb:first-page>765</sb:first-page>
<sb:last-page>771</sb:last-page>
</sb:pages>
</sb:host>
</sb:reference>
</ce:bib-reference>
</ce:bibliography-sec>
</ce:bibliography>
</tail>
</article>
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<mods version="3.6"><titleInfo lang="en"><title>Optical character recognition by a neural network</title>
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<titleInfo type="alternative" lang="en" contentType="CDATA"><title>Optical character recognition by a neural network</title>
</titleInfo>
<name type="personal"><namePart type="given">Michael</namePart>
<namePart type="family">Sabourin</namePart>
<affiliation>INRS-Télécommunications, Canada</affiliation>
<description>Bell-Northern Research</description>
<role><roleTerm type="text">author</roleTerm>
</role>
</name>
<name type="personal"><namePart type="given">Amar</namePart>
<namePart type="family">Mitiche</namePart>
<affiliation>INRS-Télécommunications, Canada</affiliation>
<description>Requests for reprints should be sent to Amar Mitiche, INRS-Télécommunications, 3 Place du Commerce, Ile des Soeurs, Verdun, Que., Canada, H3E 1H6.</description>
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<dateValid encoding="w3cdtf">1992-01-16</dateValid>
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<abstract lang="en">An optical character recognition (OCR) system, which uses a multilayer perceptron (MLP) neural network classifier, is described. The neural network classifier has the advantage of being fast (highly parallel), easily trainable, and capable of creating arbitrary partitions of the input feature space. Issues in design of the neural network that we examine include the selection of input features, the choice of network learning and momentum parameters, and the selection of training patterns. We also provide a detailed analysis of the learning parameters to provide insight into the MLP, and to suggest a mechanism to automatically tune these parameters. An OCR neural network classifier was trained to recognize characters from a large number of fonts, thereby approaching an omnifont environment. Samples were selected from over 200 fonts and 50 typical office documents, for a total of 110,000 training patterns. In order to evaluate the performance of the MLP classifier, a comparison is made with a high performance dynamic contour warping (DCW) classifier. The base recognition rate on the test set is 96.7% for the neural network classifier, compared to 95.9% for the DCW classifier.</abstract>
<note type="content">Section title: Original Contribution</note>
<subject lang="en"><genre>Keywords</genre>
<topic>OCR</topic>
<topic>Multilayer perceptron</topic>
<topic>Learning parameters</topic>
<topic>Momentum</topic>
<topic>Hierarchical classifier</topic>
</subject>
<relatedItem type="host"><titleInfo><title>Neural Networks</title>
</titleInfo>
<titleInfo type="abbreviated"><title>NN</title>
</titleInfo>
<genre type="Journal">journal</genre>
<originInfo><dateIssued encoding="w3cdtf">199209</dateIssued>
</originInfo>
<identifier type="ISSN">0893-6080</identifier>
<identifier type="PII">S0893-6080(05)X8134-5</identifier>
<part><detail type="volume"><number>5</number>
<caption>vol.</caption>
</detail>
<detail type="issue"><number>5</number>
<caption>no.</caption>
</detail>
<extent unit="issue pages"><start>735</start>
<end>867</end>
</extent>
<extent unit="pages"><start>843</start>
<end>852</end>
</extent>
</part>
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<identifier type="istex">A31C67A1D4C0AE57B33081612CC57515B54B078E</identifier>
<identifier type="DOI">10.1016/S0893-6080(05)80144-3</identifier>
<identifier type="PII">S0893-6080(05)80144-3</identifier>
<identifier type="ArticleID">05801443</identifier>
<accessCondition type="use and reproduction" contentType="">© 1992Pergamon Press Ltd.</accessCondition>
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