問題描述

背景

對于我在大學的最后一個項目，我正在開發一個車牌檢測應用程序.我認為自己是一名中級程序員，但是我的數學知識缺乏中學以上的任何知識，這使得生成正確的公式比應該做的更難.

我花了很多時間查找學術論文，例如:

• 解決方案

  您可以采取多種方法，但首先想到的策略是:

  • 發現/研究:確定您可能需要識別的一組顏色和字體.如果您的樣本圖片代表了大多數英國車牌，那么您的工作就會變得更容易.例如.簡單、單一的字體和白色背景上的黑色字體
  • 代碼:嘗試識別圖像的矩形區域，其中顏色主要是白色和黑色.這不是一個非常繁重的數學問題，它應該讓您專注于車牌區域.
  • 代碼:對您的子區域進行一些清理，例如將其轉換為純黑白(單色)，并可能縮放/移動成一個漂亮、緊湊的矩形.
  • 使用 API:接下來在您的子選擇圖像區域上使用現有的 OCR(光學字符識別)算法，以便查看您是否可以閱讀文本.

  就像我說的那樣，這是許多策略中的一種，但它被認為是一種需要最少大量數學運算的策略……也就是說，如果您能找到適合您的 OCR 實現.

  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

  解決方案

  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.

  這篇關于車牌檢測有哪些好的算法?的文章就介紹到這了，希望我們推薦的答案對大家有所幫助，也希望大家多多支持html5模板網！

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