车牌识别系统的设计(Python)

车牌识别系统的设计(Python)(论文12000字,程序)
摘要：随着智能化的推进，越来越多的单调却任务量繁重的工作被计算机所替代。因为车牌作为车辆的唯一识别标志在交通智能化领域，车牌识别技术首当其冲，扮演着至关重要的角色。车牌识别系统借助于高性能的CPU，GPU硬件，还有计算机视觉、人工智能等高新技术，从图像中定位出车牌的位置，提取并对车牌号的内容加以识别。本文中的车牌识别系统包括：图像预处理、车牌提取、车牌矫正、字符分割、字符识别等模块。本文重点实现了最常见轿车上的7字蓝底白字车牌识别。在字符分割环节，本文针对图片中车牌倾斜情况，进行具体的矫正优化，达到了较好的识别效果。
关键字：车牌识别; Python;OpenCV
 
Design and Implementation of License Plate Recognition System
Abstract: With the advancement of intelligence, more and more monotonous but heavy workloads have been replaced by computers. Because the license plate as the vehicle's unique identifier in the field of traffic intelligence, license plate recognition technology is the first to play a crucial role. The license plate recognition system uses the high-performance CPU, GPU hardware, computer vision, artificial intelligence and other high technologies to locate the position of the license plate from the image, to extract and recognize the content of the license plate number. The license plate recognition system in this paper includes: image preprocessing, license plate extraction, license plate rectification, character segmentation, character recognition and other modules. This article focuses on the realization of the 7-character with blue-white license plate recognition on the most common cars. In the character segmentation segment, this paper aims at the license plate tilting situation in the picture, carries on the concrete correction optimization, has achieved the good recognition effect. [版权所有：http://DOC163.com]
Key Words: License Plate Recognition; Python; OpenCV
  [来源：http://www.doc163.com]

目 录
1 绪论    1
1.1 研究目的    1
1.2 研究意义    1

[资料来源：www.doc163.com]

1.3 国内外研究现状    1
2 需求分析    2
3 可行性分析    2
3.1 技术可行性    2
3.2 经济可行性    2
3.3 社会可行性    3
4 总体处理流程    3
4.1 车牌识别流程    3
4.2系统功能具体分析    3
4.2.1 图像采集    3
4.2.2图像归一化处理    4
4.2.3车牌定位    4
4.2.4 字符分割    5
4.2.5 字符识别    5
4.4 开发使用的库    6
4.4.1 Python 与 OpenCV    6
4.4.2 Matplotlib与 NumPy    7
5 详细处理操作    7
5.1 图像预处理    7
5.2 车牌定位    8
5.2.1 图像去噪    9
5.2.2 找边缘、二值化    11
5.2.3 闭操作、形态学    13
5.2.4 图像矫正、归一化    15 [资料来源：Doc163.com]
5.2.5  使用HSV颜色空间筛选    17
5.3 字符分割    19
5.4 字符识别    23
5.4.1训练SVM    23
5.4.2 使用SVM识别    25
6车牌识别系统实现    25
6.1 选择待识别车牌    25
6.2 预处理    26
6.3 车牌定位    27
6.4 字符分割    28
6.5 字符识别    28
7 总结与展望    29
参考文献    29
致谢    30 [资料来源：Doc163.com]