什么是一些受欢迎的OCR算法？ [英] What are some popular OCR algorithms?
问题描述
我一直感兴趣的机器学习和计算机视觉一段时间，所以我决定尝试建立在C＃中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#.
我在找一些常见的OCR算法描述，我怎么会去用C＃实现它们。这是一个学习的过程，所以我的不的寻找一个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.
任何信息将AP preciated，谢谢。
Any information would be appreciated, thanks.
推荐答案
OCR是一个非常广泛的领域，这包括设置图像归一化（直方图均衡，颜色去除），特征提取（纹理，线段，边缘检测），并模式分类/学习机（神经网络，支持向量机等）。你可能需要实现至少有某种每个以上的（归一化，特征提取，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).
听起来好像要玩耍，写一些算法中的，了解OCR。如果是这样的话，有关于这个问题，你可以访问，如果你有机会获得学术刊物广泛的文献（如果没有，去最近的大学和度过一天制作复印件或打印的事情了）。
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（如果日）调查显示：
This is a decent (if dated) survey:
光学字符识别 - 一项调查。
Impedovo，S |奥塔维亚诺，L | Occhinegro，S
INT。 J. PATTERN RECOG。 ARTIF。 INTELL。卷。 5，没有。 1-2，第1-24。 1991年
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
和IEEE PAMI将是很好的地方开始。
And IEEE PAMI would be good places to start.
或者，谷歌学术搜索变成了颇多：
<一href=\"http://scholar.google.com/scholar?hl=en&lr=&safe=off&client=firefox-a&q=optical+character+recognition&btnG=Search\">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
您也可以看看在杜达，赫德和鹳切线距离下一个距离度量是（是？）的数字识别使用的一个很好的例子。
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.
您会需要一些数据一起玩，所以不是写的数字每扫描他们0-9 4000次，有一个UCI数据与所有的数字设置：
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:
<一个href=\"http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\">http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits
有关快速启动第一，尽量HIST-的EQ设置的图像，然后计算切线距离和做一些聚类，然后训练了一个简单的模式分类算法中的产生的功能。
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。
这篇关于什么是一些受欢迎的OCR算法？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
