| Summary: | 碩士 === 國立中央大學 === 資訊工程學系 === 106 === Considering that traffic/shop signs appearing in street view images contain important visual information such as locations of scenes, effects of advertising on billboards, and the information of store, etc., a text/graph detection mechanism in street view images is proposed in this research. However, many of these objects in street view images are not easy to extract with a fixed template. In addition, street view images often contain cluttered backgrounds such as buildings or trees, which may block some parts of the signs, complicating the related detection. Weather, light conditions and filming angle may also increase the challenges. Another issue is related to the Chinese writing style as the characters can be written vertically or horizontally. Detecting different directions of text-lines is one of the contributions in this research. The proposed detection mechanism is divided into two parts. A fully convolutional network (FCN) is used to train a detection model for effectively locating the positions of signs in street view images, which will be viewed as the regions of interest. The text-lines and graphs in the sign regions can then be successfully extracted by Region Proposal Network (RPN). Finally, post-processing is applied to distinguish horizontal and vertical text-lines, and eliminate false detections. Experimental results show the feasibility of the proposed scheme, especially when complex street views are investigated.
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