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access icon free Text segmentation using superpixel clustering

© The Institution of Engineering and Technology
Received 01/11/2016, Accepted 08/04/2017, Revised 28/03/2017, Published 13/04/2017

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.

Inspec keywords: image texture; image segmentation; pattern clustering; iterative methods; natural scenes; text detection; image representation; document image processing; image resolution; image enhancement

Other keywords: text image recognition; superpixel-based image representation; boundary adherence; local disturbances; natural scenes; modified density-based spatial clustering; Street View Text dataset; text image analysis; KAIST scene text dataset; ICDAR2003 natural scene text image dataset; adaptive linear iterative clustering-based text superpixel generation; text segmentation; homogeneous superpixels; text image superpixels; adaptive superpixel compactness; adaptive superpixel size; stroke superpixel verification

Subjects: Image recognition; Interpolation and function approximation (numerical analysis); Document processing and analysis techniques; Computer vision and image processing techniques; Interpolation and function approximation (numerical analysis)

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