Fractional poisson enhancement model for text detection and recognition in video frames. (April 2016)

• Record Type: Journal Article
• Authors: Roy, Sangheeta; Shivakumara, Palaiahnakote; Jalab, Hamid A.; Ibrahim, Rabha W.; Pal, Umapada; Lu, Tong
• Is Part Of: Pattern recognition. Volume 52(2016:Apr.)
• Page Start: 433
• Page End: 447
• Publication Date: 2016-04
• Subjects: Text detection -- Text recognition -- Laplacian operation -- Fractional Poission model -- Text enhancement; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2015.10.011
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: Performing Laplacian operation on video images is a common technique to improve image contrast to achieve good text detection and recognition accuracies. However, it is a fact that when Laplacian operation enhances contrast, at the same time it introduces too many noises. To alleviate this, the existing methods propose different enhancement methods and filters. In this paper, we propose a generalized enhancement model based on fractional calculus to increase the quality of images obtained by Laplacian operation. The proposed method considers edges and their neighbor information to derive a mathematical model for enhancing low contrast information in video as well as in scene images. Experimental results of text detection and recognition methods on different databases show that the proposed enhancement model improves their accuracies significantly. The enhancement model is compared with standard enhancement models to show that the proposed model outperforms the existing models in terms of quality measures. The usefulness of the proposed model is validated through text detection and recognition experiments. Highlights: A new Fractional Poisson enhancement model for noise removal in video. The model can be used for reducing distortion effects in text detection and recognition. The model enhances the results of existing text detection and recognition methods.