Title
Document image quality assessment based on texture similarity index
Document Type
Conference publication
Publication details
Alaei, A, Conte, D, Blumenstein, M & Raveaux, R 2018, 'Document image quality assessment based on texture similarity index', in Proceedings of the 12th IAPR Workshop on Document Analysis Systems (DAS), Santorini, Greece, 11-14 April, IEEE, USA, pp. 132-137. ISBN: 9781509017928
Published version available from
Peer Reviewed
Peer-Reviewed
Abstract
In this paper, a full reference document image quality assessment (FR DIQA) method using texture features is proposed. Local binary patterns (LBP) as texture features are extracted at the local and global levels for each image. For each extracted LBP feature set, a similarity measure called the LBP similarity index (LBPSI) is computed. A weighting strategy is further proposed to improve the LBPSI obtained based on local LBP features. The LBPSIs computed for both local and global features are then combined to get the final LBPSI, which also provides the best performance for DIQA. To evaluate the proposed method, two different datasets were used. The first dataset is composed of document images, whereas the second one includes natural scene images. The mean human opinion scores (MHOS) were considered as ground truth for performance evaluation. The results obtained from the proposed LBPSI method indicate a significant improvement in automatically/accurately predicting image quality, especially on the document image-based dataset.
