基于明暗度分类和改进二分法的车牌字符分割

A brightness classification and improved dichotomy based license plate segmentation algorithm

车牌字符分割易受到车牌倾斜及边界、杂点的干扰,致使复杂条件下的车牌图像分割准确率不高,针对该缺点提出一种鲁棒性强的分割算法。在车牌预处理阶段进行图像明暗度分类及灰度图增强,以此为基础进行倾斜校正及上下边界定位;在字符切分阶段采用改进二分法进行分割,之后对1,2,6,7四个字符实现了边界精确定位。实验表明,该算法实时性较好,能够有效克服车牌对比度不高、模糊、粘连和倾斜的缺点。

License plate （LP） segmentation can easily be affected by rim, noise and LP rotation, which causes low segmentation accuracy. We presented a good-robustness segmentation algorithm for the issue. The algorithm performed brightness classification and grayscale enhancement in the preprocessing stage before tilt correction and localization of the upper and lower boundaries. An improved dichotomy, algorithm was then applied to segment LP characters. We implemented precise border localization for characters 1, 2, 6 and 7. Experiment shows that the algorithm has low computational burden and can effectively overcome such negative influences as low contrast, blurness, character adhesion and LP tilt.