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首页> 外文期刊>ISIJ international >Recognition of Plate Identification Numbers Using Convolution Neural Network and Character Distribution Rules
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Recognition of Plate Identification Numbers Using Convolution Neural Network and Character Distribution Rules

机译：使用卷积神经网络和字符分布规则识别车牌识别号

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摘要

Recognition of plate identification numbers (PINs) is of much importance for the automation of the iron and steel production. The recognition of PINs in industrial site is a challenging problem due to complicated background, low quality of characters. Conventional image processing algorithms are employed to extract the numbers, but it is difficult for these methods to locate and recognize the numbers on the plates in complex industrial production by manually designed features. The end-to-end convolution neural network is employed to solve these problems by automatically extracted features. These features seldom combine the real production rules. A delicate recognition method of PINs using convolution neural network and characters distribution rules is proposed. The PINs are roughly recognized by convolution neural network with Non-Maximum Suppression (NMS), and the PINs are exactly processed using the character distribution rules. Experiment results demonstrate that the method proposed can arrive at a very high recognition rate 96.32% and improve the recognition rate by 54.07% compared with the end-to-end convolution neural network.
机译：钢板识别号（PIN）的识别对于钢铁生产的自动化非常重要。由于背景复杂，字符质量低，在工业现场识别PIN是一个具有挑战性的问题。使用常规的图像处理算法来提取数字，但是对于这些方法而言，在复杂的工业生产中，通过手动设计的功能很难在板上定位和识别数字。端到端卷积神经网络通过自动提取特征来解决这些问题。这些功能很少结合实际的生产规则。提出了一种基于卷积神经网络和字符分布规则的PIN识别方法。通过具有非最大抑制（NMS）的卷积神经网络可以粗略地识别PIN，并使用字符分配规则对PIN进行精确处理。实验结果表明，与端到端卷积神经网络相比，该方法可以达到很高的识别率96.32％，识别率提高了54.07％。

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