Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/42111
Title: A new run length based method for scene text detection
Authors: Basavanna, M.
Shivakumara, P. 
Srivatsa, S.K.
Hemantha Kumar, G.
Keywords: Complex scene text image
Max-Min clustering
Run length
Scene text detection
Scene text recognition
Sobel edge map
Issue Date: 2011
Source: Basavanna, M.,Shivakumara, P.,Srivatsa, S.K.,Hemantha Kumar, G. (2011). A new run length based method for scene text detection. Proceedings of the 5th Indian International Conference on Artificial Intelligence, IICAI 2011 : 1730-1736. ScholarBank@NUS Repository.
Abstract: Scene text detection and extraction from images is not as easy as detection of text in plain background document images because scene text images usually have complex background, different fonts, font size, colors, orientation etc. Hence it is an emerging and challenging research area in document analysis. Besides, it is essential for bridging gap between high and low level features to retrieve images accurately and efficiently. To achieve this, we present a new method based on run length algorithm for scene text detection in complex background images. The run lengths are calculated for the Sobel edge map of input image. The run length helps in detecting text because of the fact that when text appears in the image, characters and words have regular spacing between characters and words. This feature is explored to detect text in scene images by introducing run length algorithm in this work. The benchmark database ICDAR 2003 competition is used for evaluating the performance of the proposed method. The result of the proposed method is compared with the result of existing methods to show its superiority over existing methods in terms of f-measure. The robustness of the proposal is also tested by conducting experiments on our own data, captured by high resolution (camera) and low resolution (mobile).
Source Title: Proceedings of the 5th Indian International Conference on Artificial Intelligence, IICAI 2011
URI: http://scholarbank.nus.edu.sg/handle/10635/42111
ISBN: 9780972741286
Appears in Collections:Staff Publications

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