什么是一些流行的OCR算法？ [英] What are some popular OCR algorithms?
问题描述
我一直对机器学习和计算机视觉感兴趣，所以我决定尝试在C＃中构建一个简单的光学字符识别演示。
I've been interested in machine learning and computer vision for a while, so I've decided to attempt to build a simple Optical Character Recognition demo in C#.
我正在寻找一些常见的OCR算法的描述，以及我将如何在C＃中实现它们。这是一个学习练习，所以我不是寻找一个OCR库。
I'm looking for a description of some common OCR algorithms and how I would go about implementing them in C#. It's a learning exercise so I'm not looking for an OCR library.
感谢任何信息。
推荐答案
包括图像归一化（直方图均衡，颜色去除），特征提取（纹理，线段，边缘检测）和模式分类/机器学习（神经网络，支持向量机等）的非常广泛的领域。你可能需要至少实现上述的每一个（规范化，提取功能，做机器学习）。
OCR is a very broad field that includes things like image normalization (histogram equalization, color removal), feature extraction (textures, line segments, edge detection), and pattern classification / machine learning (neural networks, support vector machines, etc). You'll probably need to implement at least some sort of each of the above (normalize, extract features, do machine learning).
这听起来像你想玩，写一些算法，并了解OCR。如果是这样，有很多关于这个主题的文献，如果你有访问学术期刊，你可以访问（如果没有，去最近的大学，花一天时间做复印或打印出来）。
It sounds like you want to play around, write some algo's and learn about OCR. If that's the case, there's a wide literature on the subject that you can get access to if you have access to academic Journals (if not, go to the nearest University and spend a day making photocopies or printing things out).
这是一个体面的（如果已过时）调查：
This is a decent (if dated) survey:
光学字符识别 - 调查。
Impedovo，S | Ottaviano，L | Occhinegro，S
INT。 J.PATTERN RECOG。 ARTIF。 INTELL。 Vol。 5，否。 1-2，pp。1-24。 1991
Optical character recognition--a survey. Impedovo, S | Ottaviano, L | Occhinegro, S INT. J. PATTERN RECOG. ARTIF. INTELL. Vol. 5, no. 1-2, pp. 1-24. 1991
并且IEEE PAMI将是好的开始的地方。
And IEEE PAMI would be good places to start.
或者，Google学术搜索的搜索量很大：
http://scholar.google.com/scholar?hl=en& ; lr =& safe = off& client = firefox-a& q = optical + character + recognition& btnG = Search
Or, a google scholar search turns up quite a lot: http://scholar.google.com/scholar?hl=en&lr=&safe=off&client=firefox-a&q=optical+character+recognition&btnG=Search
Duda，Hart和Stork在Tangent Distance下的一个例子，用于数字识别的距离度量是一个很好的例子。
You might also look in Duda, Hart, and Stork under "Tangent Distance" for a good example of a distance metric that is (was?) used in digit recognition.
需要一些数据来玩，所以不是每次写入0-9 4000次数字并扫描它们，而是存在具有所有数字的UCI数据集：
You're going to need some data to play with, so instead of writing the numbers 0-9 4000 times each and scanning them, there's a UCI data set with all the numbers:
http://archive.ics.uci.edu/ml/数据集/ + +手写+数字的光学+识别+
为了快速开始，尝试对图像进行hist-eq'ing，然后计算切线距离，做一些聚类，然后训练一个简单的模式分类算法对结果的功能。
For a quick first start, try hist-eq'ing the images, then calculate tangent distances and do some clustering, then train up a simple pattern classification algo on the resulting features.
享受。
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