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牌照字符识别
牌照字符识别
License plate character recognition
方法主要有基于模板匹配算法和基于人工神经网络算法。基于模板匹配算法首先将分割后的字符二值化并将其尺寸大小缩放为字符数据库中模板的大小，然后与所有的模板进行匹配，选择最佳匹配作为结果。基于人工神经网络的算法有两种：一种是先对字符进行特征提取，然后用所获得特征来训练神经网络分配器;另一种方法是直接把图像输入网络，由网络自动实现特征提取直至识别出结果。
The main methods are template matching algorithm and artificial neural network algorithm. Based on the template matching algorithm, firstly, the segmented character is binarized and its size is scaled to the size of the template in the character database, then it is matched with all templates, and the best matching is selected as the result. There are two algorithms based on artificial neural network: one is to extract the features of characters, and then use the obtained features to train the neural network distributor; Another method is to input the image directly into the network, and the network automatically extracts the features until the recognition result is obtained.
实际应用中，车牌识别系统的识别率还与牌照质量和拍摄质量密切相关。牌照质量会受到各种因素的影响，如生锈、污损、油漆剥落、字体褪色、牌照被遮挡、牌照倾斜、高亮反光、多牌照、假牌照等等;实际拍摄过程也会受到环境亮度、拍摄方式、车辆速度等等因素的影响。这些影响因素不同程度上降低了车牌识别的识别率，也正是车牌识别系统假山制作 不锈钢铸件 铝箔真空袋 七孔梅花管 hdpe硅芯管 液压钢坝 PVC软管 飞行激光打标机 沥青保温泵的困难和挑战所在。为了提高识别率，除了不断地完善识别算法还应该想办法克服各种光照条件，使采集到的图像最利于识别。
In practical application, the recognition rate of license plate recognition system is also closely related to the quality of license plate and shooting. The quality of license plate will be affected by various factors, such as rust, stain, paint peeling, font fading, license plate shielding, license plate inclination, highlight and reflection, multiple license plates, false license plates, etc; The actual shooting process will also be affected by environmental brightness, shooting mode, vehicle speed and other factors. These factors reduce the recognition rate of license plate recognition to varying degrees, which is the difficulty and challenge of license plate recognition system. In order to improve the recognition rate, in addition to constantly improving the recognition algorithm, we should also find ways to overcome various lighting conditions to make the collected images most conducive to recognition.
License plate character recognition
方法主要有基于模板匹配算法和基于人工神经网络算法。基于模板匹配算法首先将分割后的字符二值化并将其尺寸大小缩放为字符数据库中模板的大小，然后与所有的模板进行匹配，选择最佳匹配作为结果。基于人工神经网络的算法有两种：一种是先对字符进行特征提取，然后用所获得特征来训练神经网络分配器;另一种方法是直接把图像输入网络，由网络自动实现特征提取直至识别出结果。
The main methods are template matching algorithm and artificial neural network algorithm. Based on the template matching algorithm, firstly, the segmented character is binarized and its size is scaled to the size of the template in the character database, then it is matched with all templates, and the best matching is selected as the result. There are two algorithms based on artificial neural network: one is to extract the features of characters, and then use the obtained features to train the neural network distributor; Another method is to input the image directly into the network, and the network automatically extracts the features until the recognition result is obtained.
实际应用中，车牌识别系统的识别率还与牌照质量和拍摄质量密切相关。牌照质量会受到各种因素的影响，如生锈、污损、油漆剥落、字体褪色、牌照被遮挡、牌照倾斜、高亮反光、多牌照、假牌照等等;实际拍摄过程也会受到环境亮度、拍摄方式、车辆速度等等因素的影响。这些影响因素不同程度上降低了车牌识别的识别率，也正是车牌识别系统假山制作 不锈钢铸件 铝箔真空袋 七孔梅花管 hdpe硅芯管 液压钢坝 PVC软管 飞行激光打标机 沥青保温泵的困难和挑战所在。为了提高识别率，除了不断地完善识别算法还应该想办法克服各种光照条件，使采集到的图像最利于识别。
In practical application, the recognition rate of license plate recognition system is also closely related to the quality of license plate and shooting. The quality of license plate will be affected by various factors, such as rust, stain, paint peeling, font fading, license plate shielding, license plate inclination, highlight and reflection, multiple license plates, false license plates, etc; The actual shooting process will also be affected by environmental brightness, shooting mode, vehicle speed and other factors. These factors reduce the recognition rate of license plate recognition to varying degrees, which is the difficulty and challenge of license plate recognition system. In order to improve the recognition rate, in addition to constantly improving the recognition algorithm, we should also find ways to overcome various lighting conditions to make the collected images most conducive to recognition.
