NDLI: Text segmentation using superpixel clustering

Content Provider	IET Digital Library
Author	Zhu, Yuanping Zhang, Kuang
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.
Starting Page	455
Ending Page	464
Page Count	10
ISSN	17519659
Volume Number	11
e-ISSN	17519667
Issue Number	Issue 7, Jul (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/11/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.0914
Journal	IET Image Processing
Publisher Date	2017-04-13
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Linear Iterative Clustering-based Text Superpixel Generation Adaptive Superpixel Compactness Adaptive Superpixel Size Boundary Adherence Computer Vision And Image Processing Technique Document Image Processing Document Processing Technique Homogeneous Superpixels ICDAR2003 Natural Scene Text Image Dataset Image Enhancement Image Recognition Image Representation Image Resolution Image Segmentation Image Texture Interpolation And Function Approximation Iterative Method KAIST Scene Text Dataset Local Disturbances Modified Density-based Spatial Clustering Natural Scenes Numerical Analysis Pattern Clustering Street View Text Dataset Stroke Superpixel Verification Superpixel-based Image Representation Text Detection Text Image Analysis Text Image Recognition Text Image Superpixels Text Segmentation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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