| Summary: | 碩士 === 國立交通大學 === 資訊工程系 === 91 === In this paper, a recognition system is proposed to extract and recognize license plates of motorcycles on highways. Images are captured by a fixed camera under outdoor environments. The images may have more than one vehicle and different sizes of license plates due to the variation of distances from the camera to the vehicles. The system comprised three stages: moving object detection, license plate extraction and license plate recognition.
In the first stage, a block-difference method is proposed to detect moving objects, which are possibly the motorcycles on highways. Without using all pixels in images, each image is tessellated into M X N blocks. According to the variance and the similarity of blocks defined on the two diagonal lines, the blocks are categorized as three kinds: low-contrast, stationary and moving blocks. Some misclassified blocks are corrected by the mathematical morphological method.
In the second stage, a projection-based method is used to find two peaks in the projection histogram to bound license plates. A screening method is first used to extract license plates. The edge magnitudes are used to locate license plates since the contrast between the colors of backgrounds and characters in the license plates is sharp. Edges are classified into vertical edges and horizontal edges and form four projection histograms: the vertical projection of vertical edges, the horizontal projection of vertical edges, the vertical projection of horizontal edges and the horizontal projection of horizontal edges. Because license plates have many edge pixels, the count of the scanning line across license plates in the projection histograms will be high. So, some scanning lines with low counts can be removed by using information of the four projection histograms. Finally, the license plates which incorrectly segmented into many blocks can be restored by the character height obtained from the projection profile.
In the third stage, character images in the license plates are segmented. Because the license plates may be skew, the skew angle of a license plate is determined from the plate''s bottom border. The character widths are the same and are four times of the dot "-" width. The plate region width is divided into 25(6*4+1) units and the characters are cut in the computed locations. The recognition kernel comprises three kinds of features: contour directional, crossing count and peripheral background area features. Finally, the regulations of the license plates of motorcycles are used to correct errors.
In our experiments, we tested 180 pairs of images. The block-difference method for moving object detection has 98% success rates, is very fast compared with the previous methods and can remove 88 percent of pixels from an image in average. The screening method for license plate extraction has 94.4% success rates. The recognition rate for characters in license plates is 90.2%.
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