Please use this identifier to cite or link to this item: https://doi.org/10.1142/S0218001412550105
Title: Multi-oriented text detection in scene images
Authors: Basavanna, M.
Shivakumara, P. 
Srivatsa, S.K.
Kumar, G.H.
Keywords: boundary growing
Multi-oriented text
run-lengths for multi-oriented text
scene text detection
zero crossing
Issue Date: 2012
Citation: Basavanna, M., Shivakumara, P., Srivatsa, S.K., Kumar, G.H. (2012). Multi-oriented text detection in scene images. International Journal of Pattern Recognition and Artificial Intelligence 26 (7). ScholarBank@NUS Repository. https://doi.org/10.1142/S0218001412550105
Abstract: We present a new run-length based method for multi-oriented text detection in scene images. We consider one ideal Sobel edge image of the horizontal text image to compute run-lengths for multi-oriented text images. Then the method proposes a Max-Min clustering to find ideal run-lengths that represents text pixel from an array of run-lengths of ideal image. The run-lengths computed for the input multi-oriented and horizontal text images are matched with the ideal run-lengths given by the Max-Min clustering to find potential run-lengths. The boundary growing method is introduced to traverse multi-oriented text lines given by the potential run-lengths and then the method eliminates false positives to clear the background using angle-proximity features of the text blocks. The non-horizontal text image is rotated to horizontal direction based on angle of the text lines to ease the implementation. The method explore new idea based on zero-crossing to separate text lines from the touching text lines given by the boundary growing method. The proposed method is tested on our own multi-oriented scene data captured by high resolution camera and mobile camera, and the benchmark database (ICDAR 2003 competition scene images) to evaluate the performance of the proposed method. The results are compared with the existing methods to show that the proposed method outperforms the existing methods in terms of measures. © World Scientific Publishing Company.
Source Title: International Journal of Pattern Recognition and Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/39113
ISSN: 02180014
DOI: 10.1142/S0218001412550105
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