问题描述

背景

对于我在大学的最后一个项目，我正在开发一个车牌检测应用程序.我认为自己是一名中级程序员，但是我的数学知识缺乏中学以上的任何知识，这使得生成正确的公式比应该做的更难.

我花了很多时间查找学术论文，例如:

• 解决方案

  您可以采取多种方法，但首先想到的策略是:

  • 发现/研究:确定您可能需要识别的一组颜色和字体.如果您的样本图片代表了大多数英国车牌，那么您的工作就会变得更容易.例如.简单、单一的字体和白色背景上的黑色字体
  • 代码:尝试识别图像的矩形区域，其中颜色主要是白色和黑色.这不是一个非常繁重的数学问题，它应该让您专注于车牌区域.
  • 代码:对您的子区域进行一些清理，例如将其转换为纯黑白(单色)，并可能缩放/移动成一个漂亮、紧凑的矩形.
  • 使用 API:接下来在您的子选择图像区域上使用现有的 OCR(光学字符识别)算法，以便查看您是否可以阅读文本.

  就像我说的那样，这是许多策略中的一种，但它被认为是一种需要最少大量数学运算的策略……也就是说，如果您能找到适合您的 OCR 实现.

  Background

  For my final project at university, I'm developing a vehicle license plate detection application. I consider myself an intermediate programmer, however my mathematics knowledge lacks anything above secondary school, which makes producing the right formulas harder than it probably should be.

  I've spend a good amount of time looking up academic papers such as:

  • Detecting Vehicle License Plates in Images
  • Robust License Plate Detection using Image Saliency
  • Local Enhancement of Car Image for License Plate Detection

  When it comes to the math, I'm lost. Due to this testing various graphic images proved productive, for example:

  to

  However this approach only worked to that particular image, and if the techniques were applied to different images, I'm sure a poorer conversion would occur. I've read about a formula called the "bottom hat morphology transform", which does the following:

  Basically, the trans- formation keeps all the dark details of the picture, and eliminates everything else (including bigger dark regions and light regions).

  I can't find much information on this, however the image within the documentation near the end of the report shows its effectiveness.

  Other constraints

  • Developing in C#
  • Confining the project to UK registration plates only
  • I can choose the images to convert as a demonstration

  Question

  I need advice on what transformation techniques I should focus on developing, and what algorithms can help me.

  EDIT: New information present on Continued - Vehicle License Plate Detection

  解决方案

  There are a number of approaches you can take but the first strategy that pops into mind is to:

  • Discovery/research: Identify the set of colors and fonts that you may need to identify. If your sample picture is representative of most British plates then your job is made easier. E.g. Simple, singular font and black lettering on a white background
  • Code: Attempt to identify a rectangular region of an image where the colors are predominantly white and black. This is not a terribly math-heavy problem and it should give you the license plate region to concentrate on.
  • Code: Do some clean up on your subregion such conversion to pure black and white (monochrome) and perhaps scaling/shifting into a nice, tight rectangle.
  • Use API: Next employ an existing OCR (optical character recognition) algorithm on your sub-selected image region so see if you can read the text.

  Like I said, this is one strategy of many but it comes to mind as one requiring the least amount of heavy math... that is if you can find an OCR implementation that will work for you.

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