Handwritten and Printed Text Separation in Real Document
Identifieur interne : 001225 ( Main/Exploration ); précédent : 001224; suivant : 001226Handwritten and Printed Text Separation in Real Document
Auteurs : Abdel Belaïd [France] ; Santosh K. C. [France] ; Vincent Poulain D'Andecy [France]Source :
Abstract
The aim of the paper is to separate handwritten and printed text from a real document embedded with noise, graphics including annotations. Relying on run-length smoothing algorithm (RLSA), the extracted pseudo-lines and pseudo-words are used as basic blocks for classification. To handle this, a multi-class support vector machine (SVM) with Gaussian kernel performs a first labelling of each pseudo-word including the study of local neighbourhood. It then propagates the context between neighbours so that we can correct possible labelling errors. Considering running time complexity issue, we propose linear complexity methods where we use k-NN with constraint. When using a kd-tree, it is almost linearly proportional to the number of pseudo-words. The performance of our system is close to 90%, even when very small learning dataset are used, where samples are basically composed of complex administrative documents.
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Le Sequoïa 30470 Aimargues</addrLine><country key="FR"></country></address><ref type="url">http://www.itesoft.fr</ref></desc><listRelation><relation active="#struct-365824" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-365824" type="direct"><org type="institution" xml:id="struct-365824" status="INCOMING"><orgName>ITESOFT</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation><country>France</country></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The aim of the paper is to separate handwritten and printed text from a real document embedded with noise, graphics including annotations. Relying on run-length smoothing algorithm (RLSA), the extracted pseudo-lines and pseudo-words are used as basic blocks for classification. To handle this, a multi-class support vector machine (SVM) with Gaussian kernel performs a first labelling of each pseudo-word including the study of local neighbourhood. It then propagates the context between neighbours so that we can correct possible labelling errors. Considering running time complexity issue, we propose linear complexity methods where we use k-NN with constraint. When using a kd-tree, it is almost linearly proportional to the number of pseudo-words. The performance of our system is close to 90%, even when very small learning dataset are used, where samples are basically composed of complex administrative documents.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Belaid, Abdel" sort="Belaid, Abdel" uniqKey="Belaid A" first="Abdel" last="Belaïd">Abdel Belaïd</name></region><name sortKey="K C, Santosh" sort="K C, Santosh" uniqKey="K C S" first="Santosh" last="K. C.">Santosh K. C.</name><name sortKey="Poulain D Andecy, Vincent" sort="Poulain D Andecy, Vincent" uniqKey="Poulain D Andecy V" first="Vincent" last="Poulain D'Andecy">Vincent Poulain D'Andecy</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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