基于卷积神经网络的Android车牌识别方法实现

I. Astawa, I. Caturbawa, E. Rudiastari, M. Radhitya, Ni Kadek Dessy Hariyanti
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引用次数: 1

摘要

每辆车都配备了一个车牌形式的身份。在检查时经常会发现伪造的文件。随着人工智能技术的发展,特别是车牌识别领域允许使用移动设备进行车牌识别。使用Android应用程序提供了许多优点,例如更高的识别精度、更少的资源消耗和更少的计算复杂性。在本研究中,使用卷积神经网络(CNN)进行车牌字符识别是深度学习方法之一。字符识别过程是通过对车牌中的字符进行分割来实现的。接下来是用CNN方法提取字符的过程。字符提取的结果是特征的形式。将字符特征与预先准备好的字符特征库进行匹配。测试结果非常令人满意,94%的对应字符和6%的不合适字符。
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Convolutional Neural Network Method Implementation for License Plate Recognition in Android
Each vehicle is equipped with an identity in the form of a number plate. Counterfeit documents are often found at the time of examination. Along with the development of artificial intelligence technology, especially in the field of number plate recognition allowing number plate recognition using mobile devices. Using the Android application provides many advantages such as higher recognition accuracy, less resource consumption, and less computational complexity. In this study, the character recognition of vehicle number plates using Convolutional Neural Network (CNN) is one of the deep learning methods. The character recognition process is realized by the segmentation process, which is taking the characters in the number plate. Next is the process of extracting characters with the CNN method. Character extraction results in the form of features. Character features are matched with the pre-prepared character feature database. The test results are very satisfying, which is 94% of the corresponding characters and 6% of characters are not suitable.
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