NDLI: An effective graph-cut scene text localization with embedded text segmentation

Content Provider	Springer Nature Link
Author	Liu, Xiaoqian Wang, Weiqiang
Copyright Year	2014
Abstract	This paper presents an effective and efficient approach to extracting scene text from images. The approach first extracts the edge information by the local maximum difference filter (LMDF), and at the same time a given image is decomposed into a group of image layers by color clustering. Then, through combining the characteristics of geometric structure and spatial distribution of scene text with the edge map, the candidate text image layers are identified. Further, in character level, the candidate text connected components are identified using a set of heuristic rules. Finally, the graph-cut computation is utilized to identify and localize text lines with arbitrary directions. In the proposed approach, the segmentation of text pixels is efficiently embedded into the computation of text localization as a part. The comprehensive evaluation experiments are performed on four challenging datasets (ICDAR 2003, ICDAR 2011, MSRA-TD500 and The Street View Text (SVT)) to verify the validation of our approach. In the comparison experiments with many state-of-the-art methods, the results demonstrate that our approach can effectively handle scene text with diverse fonts, sizes, colors, different languages, as well as arbitrary orientations, and it is robust to the influence of illumination change.
Starting Page	4891
Ending Page	4906
Page Count	16
File Format	PDF
ISSN	13807501
Journal	Multimedia Tools and Applications
Volume Number	74
Issue Number	13
e-ISSN	15737721
Language	English
Publisher	Springer US
Publisher Date	2014-01-12
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Scene text Text localization Text segmentation Graph-cut Multimedia Information Systems Computer Communication Networks Data Structures, Cryptology and Information Theory Special Purpose and Application-Based Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software Media Technology

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