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Method for recognizing license plate from picture

A technology in pictures and license plates, applied in the field of license plate recognition, can solve the problem of low recognition rate and achieve the effect of high license plate recognition rate

Inactive Publication Date: 2017-06-13
HOHAI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

These existing license plate recognition methods often cannot adapt to the blurred license plate caused by various complex environments, so the recognition rate is low

Method used

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  • Method for recognizing license plate from picture
  • Method for recognizing license plate from picture
  • Method for recognizing license plate from picture

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0069] In this embodiment, the license plate recognition module can perform license plate recognition under a variety of environmental conditions, such as dim lights, rainy and snowy weather, large-angle license plate, license plate defacement and other conditions. Except that the license plate recognition effect is not good under the condition of license plate defacement, the requirements can be fulfilled under other conditions. The recognition effects of various conditions are given below.

[0070] Image 6 Recognition effect under dim lighting conditions. Under dim lighting conditions, using color positioning can better distinguish the position of the license plate. And after many experiments, the effect of dim light conditions on license plate recognition is relatively low.

[0071] Figure 7 For the rainy day recognition effect, the rainy day has a greater impact on the license plate recognition, and a high-definition camera can be used to take pictures to improve the ...

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Abstract

The invention discloses a method for recognizing a license plate from a picture. The method comprises the following steps of adopting license plate positioning to determine a rectangular picture including the license plate, adopting character segmentation to respectively extract characters of a license plate number in the license plate and recognizing the characters in the license plate number based on a neural network model. According to the method, the license plate positioning is realized through combination of color positioning and Sobel positioning; through carrying out color judgment on the picture of the license plate, carrying out binaryzation and extracting a character outline and a circumscribed rectangle, the character segmentation is realized; and the characters of the license plate are recognized through a neural network algorithm. The experimental verification shows that the method of the invention has higher license plate recognition rate and can adapt to license plate recognition in different special circumstances.

Description

technical field [0001] The invention belongs to image processing and data mining technology, in particular to a method for recognizing license plates from pictures. Background technique [0002] The license plate recognition industry already has a certain market size, such as electronic police, parking lots, auto repair services and other fields have achieved certain applications. Generally speaking, the license plate recognition industry is in the development stage in our country. [0003] The recognition algorithm is the main bottleneck in the development of the license plate industry. The recognition accuracy is an important indicator of the quality of license plate recognition and a key factor in judging whether license plate recognition can be widely used. At present, the license plate recognition industry in my country can be described as a mixed bag. Although there are many companies engaged in license plate recognition, there are relatively few companies with core te...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/32G06K9/34
CPCG06V10/245G06V20/62G06V30/153G06V20/625
Inventor 王继民张成朱晓晓张鹏程张新华
Owner HOHAI UNIV
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