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Proceedings Paper

Metric-based no-reference quality assessment of heterogeneous document images
Author(s): Nibal Nayef; Jean-Marc Ogier
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Paper Abstract

No-reference image quality assessment (NR-IQA) aims at computing an image quality score that best correlates with either human perceived image quality or an objective quality measure, without any prior knowledge of reference images. Although learning-based NR-IQA methods have achieved the best state-of-the-art results so far, those methods perform well only on the datasets on which they were trained. The datasets usually contain homogeneous documents, whereas in reality, document images come from different sources. It is unrealistic to collect training samples of images from every possible capturing device and every document type. Hence, we argue that a metric-based IQA method is more suitable for heterogeneous documents. We propose a NR-IQA method with the objective quality measure of OCR accuracy. The method combines distortion-specific quality metrics. The final quality score is calculated taking into account the proportions of, and the dependency among different distortions. Experimental results show that the method achieves competitive results with learning-based NR-IQA methods on standard datasets, and performs better on heterogeneous documents.

Paper Details

Date Published: 8 February 2015
PDF: 12 pages
Proc. SPIE 9402, Document Recognition and Retrieval XXII, 94020L (8 February 2015); doi: 10.1117/12.2076150
Show Author Affiliations
Nibal Nayef, Univ. de La Rochelle (France)
Jean-Marc Ogier, Univ. de La Rochelle (France)

Published in SPIE Proceedings Vol. 9402:
Document Recognition and Retrieval XXII
Eric K. Ringger; Bart Lamiroy, Editor(s)

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