A double-layer license plate character recognition method based on deep learning

Abstract

The invention discloses a double-layer license plate character recognition method based on deep learning, and relates to the technical field of license plate recognition. The double-layer license plate character recognition method comprises the following steps of constructing a deep neural network model, wherein the deep neural network model comprises a character positioning network, a character synthesis network, a character recognition network, a character positioning network, a character synthesis network and a character recognition network which are sequentially connected with each other,obtaining a double-layer license plate training sample image set, training a deep neural network model, and recognizing a to-be-recognized double-layer license plate image through the trained deep neural network model. According to the present invention, the deep neural network model is smaller in memory consumption, smaller in calculation amount, more accurate in license plate character recognition result and higher in robustness, and the defects that a traditional license plate character recognition effect is poor and the robustness is low are overcome.

Description

technical field [0001] The invention relates to the technical field of license plate recognition, in particular to a deep learning-based double-layer license plate character recognition method. Background technique [0002] License plate character recognition is the last step of the license plate recognition system, and it is also a crucial step, which directly affects the recognition accuracy and efficiency of the entire system. In recent years, with the emergence of new technology methods, for various single-layer license plate images, such as China's single-layer blue card, single-layer yellow card, new energy license plate, etc., there are many mature methods at home and abroad, which can achieve high Accuracy. However, there are few literatures or scientific research institutions to study the double-layer license plate in China. The main reason is that the double-layer license plate is more complicated than the single-layer license plate. First, the double-layer licens...

AI Technical Summary

Problems solved by technology

This type of method can achieve very good results for clear double-layer license plates. However, for double-layer license plates with low resolution, blurred edges, and inclined characters, this type of method often has poor results and cannot accurately correct the position of the license plate. Character segmentation, license plate character recognition, etc.

[0004] In recent years, deep learning technology has been widely concerned and applied in various fields because it can simulate the neural network of the human brain and perform accurate nonlinear prediction. However, the disadvantage of this technology is that the model consumes a lot of memory and the amount of calculation is very large Large, so it needs to consume a lot of memory and computing power. At the same time, for double-layer license plates with low resolution, blurred edges, and inclined characters, the recognition effect is poor and the robustness is low.

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