Word–Wise Script Identification from Indian Documents
Identifieur interne : 000491 ( Istex/Curation ); précédent : 000490; suivant : 000492Word–Wise Script Identification from Indian Documents
Auteurs : Suranjit Sinha [Inde] ; Umapada Pal [Inde] ; B. Chaudhuri [Inde]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2004.
Abstract
Abstract: In a country like India, a single text line of most of the official documents contains two different script words. Under two-language formula, the Indian documents are written in English and the state official language. For Optical Character Recognition (OCR) of such a document page, it is necessary to separate different script words before feeding them to the OCRs of individual scripts. In this paper a robust technique is proposed to extract word-wise script identification from Indian doublet form documents. Here, at first, the document is segmented into lines and then the lines are segmented into words. Using different topological and structural features (like number of loops, headline feature, water reservoir concept based features, profile features, etc.) individual script words are identified from the documents. The proposed scheme is tested on 24210 words of different doublets and we received more than 97% accuracy, on average.
Url:
DOI: 10.1007/978-3-540-28640-0_29
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Road, 700 108, Kolkata, India</mods:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Computer Vision and Pattern Recognition Unit, Indian Statistical Unit, 203 B.T. Road, 700 108, Kolkata</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: umapada@isical.ac.in</mods:affiliation><country wicri:rule="url">Inde</country></affiliation></author><author><name sortKey="Chaudhuri, B" sort="Chaudhuri, B" uniqKey="Chaudhuri B" first="B." last="Chaudhuri">B. Chaudhuri</name><affiliation wicri:level="1"><mods:affiliation>Computer Vision and Pattern Recognition Unit, Indian Statistical Unit, 203 B.T. Road, 700 108, Kolkata, India</mods:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Computer Vision and Pattern Recognition Unit, Indian Statistical Unit, 203 B.T. 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Road, 700 108, Kolkata, India</mods:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Computer Vision and Pattern Recognition Unit, Indian Statistical Unit, 203 B.T. Road, 700 108, Kolkata</wicri:regionArea></affiliation></author><author><name sortKey="Pal, Umapada" sort="Pal, Umapada" uniqKey="Pal U" first="Umapada" last="Pal">Umapada Pal</name><affiliation wicri:level="1"><mods:affiliation>Computer Vision and Pattern Recognition Unit, Indian Statistical Unit, 203 B.T. Road, 700 108, Kolkata, India</mods:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Computer Vision and Pattern Recognition Unit, Indian Statistical Unit, 203 B.T. Road, 700 108, Kolkata</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: umapada@isical.ac.in</mods:affiliation><country wicri:rule="url">Inde</country></affiliation></author><author><name sortKey="Chaudhuri, B" sort="Chaudhuri, B" uniqKey="Chaudhuri B" first="B." last="Chaudhuri">B. Chaudhuri</name><affiliation wicri:level="1"><mods:affiliation>Computer Vision and Pattern Recognition Unit, Indian Statistical Unit, 203 B.T. Road, 700 108, Kolkata, India</mods:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Computer Vision and Pattern Recognition Unit, Indian Statistical Unit, 203 B.T. Road, 700 108, Kolkata</wicri:regionArea></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2004</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">DF8BFCAE28D0DD31D95FD2F67000772E8B8DB97E</idno><idno type="DOI">10.1007/978-3-540-28640-0_29</idno><idno type="ChapterID">29</idno><idno type="ChapterID">Chap29</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: In a country like India, a single text line of most of the official documents contains two different script words. Under two-language formula, the Indian documents are written in English and the state official language. For Optical Character Recognition (OCR) of such a document page, it is necessary to separate different script words before feeding them to the OCRs of individual scripts. In this paper a robust technique is proposed to extract word-wise script identification from Indian doublet form documents. Here, at first, the document is segmented into lines and then the lines are segmented into words. Using different topological and structural features (like number of loops, headline feature, water reservoir concept based features, profile features, etc.) individual script words are identified from the documents. The proposed scheme is tested on 24210 words of different doublets and we received more than 97% accuracy, on average.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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