Search results for "Document classification"

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ICDAR 2021 Competition on Historical Document Classification

2021

International audience; This competition investigated the performance of historical document classification. The analysis of historical documents is a difficult challenge commonly solved by trained humanists. We provided three different classification tasks, which can be solved individually or jointly: font group/script type, location, date. The document images are provided by several institutions and are taken from handwritten and printed books as well as from charters. In contrast to previous competitions, all participants relied upon Deep Learning based approaches. Nevertheless, we saw a great performance variety of the different submitted systems. The easiest task seemed to be font grou…

Historical document imagesbusiness.industryComputer scienceDocument classificationDeep learningContrast (statistics)computer.software_genreVariety (linguistics)Task (project management)Competition (economics)Document classification[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingDocument analysisFontComputingMethodologies_DOCUMENTANDTEXTPROCESSINGDatingArtificial intelligence[SHS.HIST]Humanities and Social Sciences/HistorybusinesscomputerNatural language processingHistorical document
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Weights Space Exploration Using Genetic Algorithms for Meta-classifier in Text Document Classification

2012

Text document classificationGeneral Computer ScienceComputer sciencebusiness.industryArtificial intelligenceElectrical and Electronic EngineeringbusinessMachine learningcomputer.software_genreClassifier (UML)computerSpace explorationStudies in Informatics and Control
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Aspects Concerning SVM Method’s Scalability

2008

In the last years the quantity of text documents is increasing continually and automatic document classification is an important challenge. In the text document classification the training step is essential in obtaining a good classifier. The quality of learning depends on the dimension of the training data. When working with huge learning data sets, problems regarding the training time that increases exponentially are occurring. In this paper we are presenting a method that allows working with huge data sets into the training step without increasing exponentially the training time and without significantly decreasing the classification accuracy.

Text document classificationStructured support vector machinebusiness.industryComputer scienceDocument classificationcomputer.software_genreSupport vector machineText miningScalabilityData miningbusinessCluster analysiscomputerClassifier (UML)
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