@Article{jai.2020.09955,
AUTHOR = {Xueming Qiao, Yingxue Xia, Weiyi Zhu, Dongjie Zhu, Liang Kong, Chunxu Lin, Zhenhao Guo, Yiheng Sun},
TITLE = {A Method of Text Extremum Region Extraction Based on JointChannels},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {2},
YEAR = {2020},
NUMBER = {1},
PAGES = {29--37},
URL = {http://www.techscience.com/jai/v2n1/39513},
ISSN = {2579-003X},
ABSTRACT = {Natural scene recognition has important significance and value in the fields of 
image retrieval, autonomous navigation, human-computer interaction and industrial 
automation. Firstly, the natural scene image non-text content takes up relatively high 
proportion; secondly, the natural scene images have a cluttered background and complex 
lighting conditions, angle, font and color. Therefore, how to extract text extreme regions 
efficiently from complex and varied natural scene images plays an important role in natural 
scene image text recognition. In this paper, a Text extremum region Extraction algorithm 
based on Joint-Channels (TEJC) is proposed. On the one hand, it can solve the problem 
that the maximum stable extremum region (MSER) algorithm is only suitable for gray 
images and difficult to process color images. On the other hand, it solves the problem that 
the MSER algorithm has high complexity and low accuracy when extracting the most 
stable extreme region. In this paper, the proposed algorithm is tested and evaluated on the 
ICDAR data set. The experimental results show that the method has superiority.},
DOI = {10.32604/jai.2020.09955}
}