Combining Focus Measure Operators to Predict OCR Accuracy in Mobile-Captured Document Images - Archive ouverte HAL
Communication Dans Un Congrès Année : 2014

Combining Focus Measure Operators to Predict OCR Accuracy in Mobile-Captured Document Images

Résumé

Mobile document image acquisition is a new trend raising serious issues in business document processing workflows. Such digitization procedure is unreliable, and integrates many distortions which must be detected as soon as possible, on the mobile, to avoid paying data transmission fees, and losing information due to the inability to recapture later a document with temporary availability. In this context, out-of-focus blur is a major issue: users have no direct control over it, and it seriously degrades OCR recognition. In this paper, we concentrate on the estimation of focus quality, to ensure a sufficient legibility of a document image for OCR processing. We propose two contributions to improve OCR accuracy prediction for mobile-captured document images. First, we present 24 focus measures, never tested on document images, which are fast to compute and require no training. Second, we show that a combination of those measures enables state-of-the art performance regarding the correlation with OCR accuracy. The resulting approach is fast, robust, and easy to implement in a mobile device. Experiments are performed on a public dataset, and precise details about image processing are given.
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Dates et versions

hal-01337254 , version 1 (24-06-2016)

Identifiants

Citer

Marçal Rusiñol, Joseph Chazalon, Jean-Marc Ogier. Combining Focus Measure Operators to Predict OCR Accuracy in Mobile-Captured Document Images. 11th IAPR International Workshop on Document Analysis Systems (DAS), Apr 2014, Tours, France. pp.181-185, ⟨10.1109/DAS.2014.11⟩. ⟨hal-01337254⟩

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