Character-based automated human perception quality assessment in document images
Large degradations in document images impede their readability and deteriorate the performance of automated document processing systems. Document image quality (IQ) metrics have been defined through optical character recognition (OCR) accuracy. Such metrics, however, do not always correlate with human perception of IQ. When enhancing document images with the goal of improving readability, e.g., in historical documents where OCR performance is low and/or where it is necessary to preserve the original context, it is important to understand human perception of quality. The goal of this paper is to design a system that enables the learning and estimation of human perception of document IQ. Such a metric can be used to compare existing document enhancement methods and guide automated document enhancement. Moreover, the proposed methodology is designed as a general framework that can be applied in a wide range of applications. © 2012 IEEE.
Document imaging, feature extraction, human-machine interactions, image enhancement, learning systems, perception quantification, quality metrics
Obafemi-Ajayi, Tayo, and Gady Agam. "Character-based automated human perception quality assessment in document images." IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 42, no. 3 (2011): 584-595.
IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans