A search engine for Arabic documents
Identifieur interne : 000B04 ( Main/Exploration ); précédent : 000B03; suivant : 000B05A search engine for Arabic documents
Auteurs : T. Sari [Algérie] ; A. Kefali [Algérie]Source :
Descripteurs français
- Wicri :
- topic : Recherche documentaire.
English descriptors
- mix :
Abstract
This paper is an attempt for indexing and searching degraded document images without recognizing the textual patterns and so to circumvent the cost and the laborious effort of OCR technology. The proposed approach deal with textual-dominant documents either handwritten or printed. From preprocessing and segmentation stages, all the connected components (CC) of the text are extracted applying a bottom-up approach. Each CC is then represented with global indices such as loops, ascenders, etc. Each document will be associated an ASCII file of the codes from the extracted features. Since there is no feature extraction technique reliable enough to locate all the discriminant global indices modelling handwriting or degraded prints, we apply an approximate string matching technique based on Levenshtein distance. As a result, the search module can efficiently cope with imprecise and incomplete pattern descriptions. The test was performed on some Arabic historical documents and shown good performances.
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Affiliations:
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Le document en format XML
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Sari</name><affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-81739" status="VALID"><orgName>Laboratoire de gestion electronique de documents [Annaba]</orgName><orgName type="acronym">LabGED</orgName><desc><address><country key="DZ"></country></address></desc><listRelation><relation active="#struct-300650" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-300650" type="direct"><org type="institution" xml:id="struct-300650" status="VALID"><orgName>Université Badji Mokhtar [Annaba]</orgName><desc><address><addrLine>BP 12, 23000, Annaba</addrLine><country key="DZ"></country></address><ref type="url">http://www.univ-annaba.dz/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>Algérie</country></affiliation></author><author><name sortKey="Kefali, A" sort="Kefali, A" uniqKey="Kefali A" first="A." last="Kefali">A. Kefali</name><affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-81739" status="VALID"><orgName>Laboratoire de gestion electronique de documents [Annaba]</orgName><orgName type="acronym">LabGED</orgName><desc><address><country key="DZ"></country></address></desc><listRelation><relation active="#struct-300650" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-300650" type="direct"><org type="institution" xml:id="struct-300650" status="VALID"><orgName>Université Badji Mokhtar [Annaba]</orgName><desc><address><addrLine>BP 12, 23000, Annaba</addrLine><country key="DZ"></country></address><ref type="url">http://www.univ-annaba.dz/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>Algérie</country></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Arabic handwriting recognition</term><term>Document retrieval</term><term>handwriting segmentation</term><term>handwriting segmentation.</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Recherche documentaire</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper is an attempt for indexing and searching degraded document images without recognizing the textual patterns and so to circumvent the cost and the laborious effort of OCR technology. The proposed approach deal with textual-dominant documents either handwritten or printed. From preprocessing and segmentation stages, all the connected components (CC) of the text are extracted applying a bottom-up approach. Each CC is then represented with global indices such as loops, ascenders, etc. Each document will be associated an ASCII file of the codes from the extracted features. Since there is no feature extraction technique reliable enough to locate all the discriminant global indices modelling handwriting or degraded prints, we apply an approximate string matching technique based on Levenshtein distance. As a result, the search module can efficiently cope with imprecise and incomplete pattern descriptions. The test was performed on some Arabic historical documents and shown good performances.</div></front></TEI><affiliations><list><country><li>Algérie</li></country></list><tree><country name="Algérie"><noRegion><name sortKey="Sari, T" sort="Sari, T" uniqKey="Sari T" first="T." last="Sari">T. Sari</name></noRegion><name sortKey="Kefali, A" sort="Kefali, A" uniqKey="Kefali A" first="A." last="Kefali">A. Kefali</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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