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。
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