Abstract—In this paper, a text region extraction system with
high contrasting text images for self-driving cars is proposed.
The maximally stable extremal regions (MSER) method is
usually used to extract text regions. Images must be converted
to grayscale to process with the MSER method. However, the
performance of MSER by using grayscale images has a poor
ability of capturing regions of interest in bad conditions such as
high-contrast, low-luminance, much light reflection, and so on.
An MSER system with a contrast-limited adaptive histogram
equalization (CLAHE) instead of conventional MSER is
therefore proposed. CLAHE is utilized as a pre-processing
method in MSER to detect text regions. The proposed method
achieves a precision of 81% and a recall of 82%. However, those
for the MSER with grayscale are 63% and 55%, respectively.
Index Terms—CLAHE, MSER, stroke width transform, text
region extraction.
Seokjun Kang, Daewoong Cha, Youngwoo Kim, and Dong Seog Han are
with the School of Electronics Engineering, Kyungpook National University,
Daegu, Korea (e-mail: dshan@knu.ac.kr).
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Cite: Seokjun Kang, Daewoong Cha, Youngwoo Kim, and Dong Seog Han, "Text Region Extraction in High Contrasting Image," International Journal of Future Computer and Communication vol. 6, no. 3, pp. 106-109, 2017.
