结合CNN-GRU模型无分割车牌号码字符识别

Bhargavi Suvarnam, Viswanadha Sarma Ch
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引用次数: 14

摘要

识别是从给定图像中发现车牌细节的一种数字化图像自动化操作。由于各种因素的影响,车牌识别很难取得很好的效果。一般来说,人类可以很容易地读取车牌上的字符,但机器只有经过训练才能做到这一点。现在每天的车辆都在与日俱增,要手工记下每一个车牌号是很困难的。为了避免这种情况,采用光学字符识别(OCR)技术直接提取车牌。本文建立了CNN(卷积神经网络)-GRU(门控循环单元)模型。使用CNN进行特征提取,使用GRU进行排序,没有使用任何分割方法。最后,利用GRU单元在数据集上准备的模型设计对特征进行识别。深度学习技术比模板匹配等传统方法提高了性能。该框架的测试精度为100%,训练精度为90%。
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Combination of CNN-GRU Model to Recognize Characters of a License Plate number without Segmentation
Recognition is a genre of manipulation of digitized image automation for discovering the number plate details from a given image. Due to various factors, it is difficult to achieve great recognition results for the license plate. In general, human beings can easily read characters in license plate, but the machine cannot do until it is trained to do so. Now a day’s vehicles are increasing day by day, to note down every vehicle plate number manually is difficult. To avoid that, optical character recognition (OCR) technology is used which extracts the license plate directly. In this paper, CNN (convolution neural network) –GRU (gated recurrent unit) model is developed.CNN is used for feature extraction and GRU is used for sequencing without using any segmentation methods. Finally, the character is recognized by utilizing a model design which is prepared on the dataset by GRU unit. A deep learning technique increases performance than traditional approaches like template matching. The testing precision of the proposed framework is 100% and training accuracy is 90%.
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