Study on the extraction of the text region from natural scene images by an analysis of the edge-oriented pattern
Jae Ho Yang, Gang Seong Lee, Young Pyo Hong, Sang Hun Lee · International Journal of Engineering & Technology · 2018
Background/Objectives: In this paper, we propose a hybrid scene-detection method using an edge and textural analysis in natural scene images, and finally, we detect the text regions by removing the non-text regions through a pattern analysis of each region. Methods/Statistical analysis: The proposed algorithm is divided into the pre-processing stage and the extraction processing stage to perform the text detection. The lost texts that are improved through a histogram equalization for the minimization of the loss of the text parts that is due to light exposure are detected before the edge detection. After that, the edge is detected using the Canny operator. The detected edge is obtained in the step of applying the SWT algorithm to detect the text candidate regions. The extraction processing step is the step of removing the noise region that is detected by the pixel analysis of the SWT algorithm, and it analyzes the pattern of the text regions and then removes the non-text regions to finally detect the text regions. For the quantitative comparison of the proposed algorithm, our results are compared with the ground-truth image using the precision, recall, and F-measure. Findings: One of the existing text-detection algorithms, the edge-based method, is problematic, as, in addition to the text, the complex backgrounds and textures are detected as the edges in natural scene images. The connected component-based method is also problematic, as the non-text region is included in the text region in the process of finding the connection component. Improvements/Applications: The proposed method shows an effective text-detection result regardless of the light exposure in natural scene images compared with the conventional text-detection algorithm.