Decision fusion for improved automatic license plate recognition
Decision fusion for improved automatic license plate recognition
复制标题
决策融合改进自动车牌识别
DOI：
--
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
M. Stanciu
中科院分区：
文献类型：
--
作者：
C. Molder;M. Boșcoianu;I. Vizitiu;M. Stanciu
自动车牌识别（ALPR）是一种模式识别技术，在门禁、交通监控和执法等领域有着重要的应用。因此，许多研究都集中在创建新的算法或提高其性能。许多作者提出了基于骨架特征、神经网络或模板匹配等方法的车牌识别算法。在本文中，我们提出了一种新的方法，决策融合的几种识别方法，以及新的分类功能。分类结果被证明是显着优于单独考虑每种方法获得的。为了获得更好的结果，还考虑了语法校正。几个可训练和不可训练的决策融合规则已被考虑在内，证明在他们最好的分类方法。实验结果表明，结果是非常令人鼓舞的，通过获得一个符号的良好识别率（GRC）超过99.4%的真实的牌照数据库。
Automatic license plate recognition (ALPR) is a pattern recognition application of great importance for access, traffic surveillance and law enforcement. Therefore many studies are concentrated on creating new algorithms or improving their performance. Many authors have presented algorithms that are based on individual methods such as skeleton features, neural networks or template matching for recognizing the license plate symbols. In this paper we present a novel approach for decisional fusion of several recognition methods, as well as new classification features. The classification results are proven to be significantly better than those obtained for each method considered individually. For better results, syntax corrections are also considered. Several trainable and non-trainable decisional fusion rules have been taken into account, evidencing each of the classification methods at their best. Experimental results are shown, the results being very encouraging by obtaining a symbol good recognition rate (GRC) of more than 99.4% on a real license plate database.