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Title: Detection of curved text in video: Quad tree based method
Authors: Shivakumara, P.
Basavaraju, H.T.
Guru, D.S.
Tan, C.L. 
Keywords: Curvedtext detection
K-means clustering
Quad tree technique
Symmetry verification
Text enhancement
Issue Date: 2013
Citation: Shivakumara, P., Basavaraju, H.T., Guru, D.S., Tan, C.L. (2013). Detection of curved text in video: Quad tree based method. Proceedings of the International Conference on Document Analysis and Recognition, ICDAR : 594-598. ScholarBank@NUS Repository.
Abstract: In this paper, we address curved text detection in video through a new enhancement criterion and the use of quad tree. The proposed method makes use of the quad tree to simplify the task of handling the entire frame at each stage. The proposed method employs a novel criterion for grouping of pixels based on their R, G and B values to enhance text information. As generally, a text detection problem is a two class problem, we used k-means with k=2 to identify potential text candidate pixels. From these potential candidates, connected components are then extracted and subjected to further analysis, where symmetry property based on stroke width is used for further authentication of the text representatives. These authenticated text representatives are then exploited as seed points to restore the text information with reference to the Sobel edge frame of the original input frame. To preserve the spatial information of text pixels the concept of quad tree is applied. From these seed blocks, text lines are extracted by the use of a region growing approach driven completely based on Sobel edge map. The proposed method is tested on curved video data and Hua's horizontal video text data in terms of recall, precision, f-measure, misdetection rate and processing time. The results are compared and analyzed to show that the proposed method outperforms several existing methods in terms of accuracy and efficiency. © 2013 IEEE.
Source Title: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
ISSN: 15205363
DOI: 10.1109/ICDAR.2013.123
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