Automatic vehicle license plate recognition (AVLPR) aims at extracting the region that contains the information of vehicle license number out of an image data and then identifying the characters apart from the human intervention. This study proposed an effective AVLPR framework where detection, segmentation and recognition of various shaped license plates have been focused. For both proper visual perception and computational processing, a pre-processing technique including grey-scaling conversion combined with close arithmetic-based dilation has been defined. Both vertical and horizontal edge densities have been enumerated by kernel matrices which enable robustness in detecting various shaped and sized license plates. For better detection of candidate region, the vertical and horizontal energy mapping features combined with Gaussian smoothing filter have been used to enable detection of license plates from both high definition and lower resolution images under various illumination conditions and crowded background. For ensuring a better character segmentation rate which is the prerequisite for higher recognition rate, a blob assessment method has been defined integrated with connected component analysis. With 400 vehicle images having varying pixels, the proposed algorithm achieves 96.5, 95.6 and 94.4% accuracy, respectively, in identifying, segmenting and recognising the plate number.

中文翻译：

---

用于汽车牌照自动识别框架的基于连接组件分析的集成边缘技术

自动车牌识别（AVLPR）的目的是从图像数据中提取出包含车牌号信息的区域，然后识别人为干预以外的字符。这项研究提出了一个有效的AVLPR框架，其中各种形状的车牌的检测，分割和识别已成为重点。为了适当的视觉感知和计算处理，已经定义了包括灰度转换与基于近似算术的散瞳相结合的预处理技术。垂直和水平边缘密度都已通过核矩阵进行枚举，核矩阵可在检测各种形状和尺寸的车牌时实现鲁棒性。为了更好地检测候选区域，垂直和水平能量映射功能结合高斯平滑滤波器已被用于在各种照明条件和拥挤的背景下从高清晰度和低分辨率图像中检测车牌。为了确保更好的字符分割率，这是更高识别率的先决条件，已将斑点评估方法与关联组件分析结合在一起定义。对于400个具有变化像素的车辆图像，该算法在识别，分割和识别车牌号时分别达到96.5％，95.6％和94.4％的精度。为了确保更好的字符分割率，这是更高识别率的前提，已经定义了一种斑点评估方法，并将其与连接的分量分析相结合。对于400个具有变化像素的车辆图像，该算法在识别，分割和识别车牌号时分别达到96.5％，95.6％和94.4％的精度。为了确保更好的字符分割率，这是更高识别率的先决条件，已将斑点评估方法与关联组件分析结合在一起定义。对于400个具有变化像素的车辆图像，该算法在识别，分割和识别车牌号时分别达到96.5％，95.6％和94.4％的精度。