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 ...
详细信息
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 superpixelclustering is proposed. First, to generate accurate superpixels for text images, an adaptive simple lineariterativeclustering-basedtextsuperpixelgeneration algorithm is proposed. The adaptivesuperpixel size and compactness are calculated to enhance boundary adherence. Second, to increase the complete coverage of strokes from superpixels, superpixelclustering 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.
暂无评论