Embedded Formulas Extraction
Identifieur interne : 001C29 ( Main/Exploration ); précédent : 001C28; suivant : 001C30Embedded Formulas Extraction
Auteurs : Afef Kacem [Tunisie] ; Abdel Belaïd [France] ; Mohamed Ben Ahmed [Tunisie]Source :
Descripteurs français
Abstract
A new approach for separating mathematics from usual text is presented. Contrary to the existing methods, it is more oriented toward the segmentation than the recognition, isolating the formulas outside and inside the text lines. The objective is to delimit a part of text which could disturb the OCR application, not yet trained for formula recognition and restructuring. The method is based on an adaptive segmentation working at two levels 1) A primary labelling identifies the more characteristic symbols; 2) A secondary labelling extends the context of the symbols for delimiting the formula inside the text.Experiments done on some commonly seen mathematical documents, show that our proposed method can achieve quite satisfactory rate making mathematical formulas extraction more feasible for real-world applications. The average rate of primary labelling of mathematical symbols is about 95.3% and their secondary labelling can improve the rate about 4%. Thus, about 95% of formulas are well extracted from images of documents printed with high quality
Url:
Affiliations:
- France, Tunisie
- Lorraine
- Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Lorraine
Links toward previous steps (curation, corpus...)
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- to stream Hal, to step Curation: 000046
- to stream Hal, to step Checkpoint: 000153
- to stream Main, to step Merge: 001D28
- to stream Main, to step Curation: 001C29
Le document en format XML
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Contrary to the existing methods, it is more oriented toward the segmentation than the recognition, isolating the formulas outside and inside the text lines. The objective is to delimit a part of text which could disturb the OCR application, not yet trained for formula recognition and restructuring. The method is based on an adaptive segmentation working at two levels 1) A primary labelling identifies the more characteristic symbols; 2) A secondary labelling extends the context of the symbols for delimiting the formula inside the text.Experiments done on some commonly seen mathematical documents, show that our proposed method can achieve quite satisfactory rate making mathematical formulas extraction more feasible for real-world applications. The average rate of primary labelling of mathematical symbols is about 95.3% and their secondary labelling can improve the rate about 4%. Thus, about 95% of formulas are well extracted from images of documents printed with high quality</div></front></TEI><affiliations><list><country><li>France</li><li>Tunisie</li></country><region><li>Lorraine</li></region><settlement><li>Nancy</li></settlement><orgName><li>Institut national polytechnique de Lorraine</li><li>Université Nancy 2</li><li>Université de Lorraine</li></orgName></list><tree><country name="Tunisie"><noRegion><name sortKey="Kacem, Afef" sort="Kacem, Afef" uniqKey="Kacem A" first="Afef" last="Kacem">Afef Kacem</name></noRegion><name sortKey="Ben Ahmed, Mohamed" sort="Ben Ahmed, Mohamed" uniqKey="Ben Ahmed M" first="Mohamed" last="Ben Ahmed">Mohamed Ben Ahmed</name></country><country name="France"><region name="Lorraine"><name sortKey="Belaid, Abdel" sort="Belaid, Abdel" uniqKey="Belaid A" first="Abdel" last="Belaïd">Abdel Belaïd</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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