针对车牌字符分割过程中先验知识嵌入困难，分割过程对于前期车牌定位依赖较强的问题，提出了一种新的先验知识嵌入方法及其对应的字符分割算法。给定一种类型的车牌，利用字符的可能排列方式定义马尔可夫链中的状态，可以将车牌字符分割转化为一组马尔可夫链的前向识别过程。结合连通分量提取及垂直投影分割算法，可以有效地获取车牌的最优分割结果及其置信度。在实际应用中，该算法不依赖于前期的精确定位，对粗定位后的图像即可进行快速有效地分割。该方法统一了不同类型车牌的先验知识嵌入方法，降低了编码复杂度。在中国车牌及马来西亚车牌上的实验结果均证明，该方法有效地提高了车牌字符分割的性能。

Priori embedding for character segmentation in license plate recognition(LPR) is difficult, and the segmentation results greatly depended on the precision of license localization. Thus in this paper, we present a novel integration method of the prior knowledge and its corresponding character segmentation algorithm. Given one category, the segmentation procedure can be transformed to a set of Markov transitions where the state is defined by the characters permutation. With the combination of connected-component-based and vertical-projection-based algorithms, the optimized segmentation result and its confidence level can be obtained. One advantage of our method is that only coarse localization is enough for the segmentation algorithm. The proposed method is uniform for different license categories, which reduces the coding complexity. The performance improvement of the proposed approach is illustrated by the LPR systems applied practically in China and Malaysia, both contains multiple categories of license plates.