Text segmentation using superpixel clustering. Issue 7 (7th June 2017)

• Record Type: Journal Article
• Authors: Zhu, Yuanping; Zhang, Kuang
• Is Part Of: IET image processing. Volume 11:Issue 7(2017)
• Page Start: 455
• Page End: 464
• Publication Date: 2017-06-07
• Subjects: text detection -- document image processing -- image representation -- image enhancement -- image resolution -- image segmentation -- pattern clustering -- natural scenes -- iterative methods -- image texture; text segmentation -- text image analysis -- text image recognition -- natural scenes -- superpixel‐based image representation -- local disturbances -- text image superpixels -- adaptive linear iterative clustering‐based text superpixel generation -- adaptive superpixel size -- adaptive superpixel compactness -- boundary adherence -- homogeneous superpixels -- modified density‐based spatial clustering -- stroke superpixel verification -- KAIST scene text dataset -- ICDAR2003 natural scene text image dataset -- Street View Text dataset; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.0914
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Text segmentation is important for text image analysis and recognition; however, it is challenging due to noise and complex background in natural scenes. Superpixel‐based image representation can enhance robustness to noise and local disturbances, but conventional superpixel algorithms are difficult to obtain the complete stroke regions and accurate boundaries for text images. In this study, a text segmentation method based on superpixel clustering is proposed. First, to generate accurate superpixels for text images, an adaptive simple linear iterative clustering‐based text superpixel generation algorithm is proposed. The adaptive superpixel size and compactness are calculated to enhance boundary adherence. Second, to increase the complete coverage of strokes from superpixels, superpixel clustering merges homogeneous superpixels into larger regions for both strokes and the background. A modified density‐based spatial clustering of applications with noise is proposed. Finally, stroke superpixel verification assigns each region to a stroke or to the background and the text segmentation result is obtained. The proposed method shows promising robustness to noise and complex background textures. Experimental results on the Korea Advanced Institute of Science and Technology (KAIST) scene text dataset, International Conference on Document Analysis and Recognition (ICDAR) 2003 natural scene text image dataset and Street View Text dataset verify that this method is effective and significantly outperforms existing methods.