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- W2061454893 abstract "In this paper, we describe easily extractable features and an approach for document image retrieval and classificationat spatial level. The approach is based on the content of the image and utilizing visual similarity,it provides high speed classification of noisy text document images without optical character recognition(OCR). Our method involves a bag-of-visual words (BoVW) model on the designed descriptors and a Random-Window (RW) technique to capture the structural relationships of the spatial layout. Using the features basedon these information, we analyze different multiclass classification methods as well as ensemble classifiersmethod with Support Vector Machine (SVM) as a base learner. The results demonstrate that the proposedmethod for obtaining structural relations is competitive for noisy document image categorization." @default.
- W2061454893 created "2016-06-24" @default.
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- W2061454893 date "2014-01-01" @default.
- W2061454893 modified "2023-09-23" @default.
- W2061454893 title "Document Image Classification Via AdaBoost and ECOC Strategies Based on SVM Learners" @default.
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- W2061454893 doi "https://doi.org/10.5220/0005131502500255" @default.
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