数字数字识别 - 神经网络？其他技术？
我需要找到一些方法来识别其中包含单个数字的图像（黑白）。
到目前为止我所读到的内容，我会使用神经网络。 SOM（自组织映射）是正确的网络类型吗？
还有其他好的方法来识别我还没有想到的数字（我已经找到了很多手写字符的东西，但没有在数字上）？
I need to find some method to recognize images with a single digital number in it (black on white).
What I have read so far, I would use a neural network. Is a SOM (self organizing map) the correct network type?
Is there any other good way to to recognize digital (I have found a lot of stuff for handwritten characters, but not on digital numbers) numbers that I haven't thought of yet?
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我认为你所说的神经网络类型是多类分类器，对吧？因此，您有一些二进制输入单元（可能是从白色到黑色的颜色），并且您期望二进制输出，例如您的图像显示三。我对此不太确定，但我认为做到这一点的最佳方法之一是深度信念网络。它们使用 MNIST 数据集进行了测试（如果您搜索它，应该可以在网络上的某个地方找到..）并且它们运行得很好。训练过程有点复杂，首先在无监督的情况下逐层训练网络，然后应用反向传播算法（我认为在这种情况下是交叉熵误差）。好的，我目前正在为该任务编写一个程序（用 C 升值语言编写，所以对你来说应该不难理解）。如果您想要代码，请在此处与我联系：http://compositedevtec.tk/blog/contact/，或者在 youtube 上搜索“Hinton”（发明了这些模型）。有一段关于他解释他的技术的视频。
希望这对你有一点帮助
I think the type of neural network you´re talking about is a multiclass classifier, right? So you have some binary input units(maybe colors from white to black) and you expect a binary output, for example that your image shows a three. I´m not quite shure about that but I think one of the best methods to do that is a deep belief network. They were tested with the MNIST data set(should be aviable somewhere in the web if you search for it..) and they worked pretty well. The training process is a little complicated, you first train the network unsupervised layer by layer and than apply the backpropagation algorithm(cross entropy error in that case I think). Ok, I´m currently working on a program for that task(in C sharp so it shouldn´t be to hard for you to understand). If you want the code contact me here: http://compositedevtec.tk/blog/contact/, or search youtube for "Hinton"(invented these models). There´s a video about him explaining his technique.
Hope this helped you a little bit