训练用于OCR的前馈神经网络 [英] Training feedforward neural network for OCR

问题描述

目前，我正在学习神经网络，并且正在尝试创建一个可以训练以识别手写字符的应用程序. 对于这个问题，我使用前馈神经网络，当我训练它识别1个，2个或3个不同的字符时，它似乎可以工作.但是，当我尝试使网络学习3个以上的字符时，它将以40％-60％左右的错误百分比停滞不前.

Currently I'm learning about neural networks and I'm trying to create an application that can be trained to recognize handwritten characters. For this problem I use a feed-forward neural network and it seems to work when I train it to recognize 1, 2 or 3 different characters. But when I try to make the network learn more than 3 characters it will stagnate at a error percentage around the 40 - 60%.

我尝试使用多层和更少/更多的神经元，但是我似乎无法正确地做到这一点，现在我想知道前馈神经网络是否能够识别这么多信息.

I tried with multiple layers and less/more neurons but I can't seem to get it right, now I'm wondering if a feedforward neural network is capable of recognizing that much information.

一些统计数据:

网络类型:前馈神经网络

输入神经元:使用100(10 * 10)个网格绘制字符

Input neurons: 100 (a 10 * 10) grid is used to draw the characters

输出神经元:要重新定位的字符数

Output neurons: The amount of characters to regocnize

有人知道我的体系结构中可能存在的缺陷吗?输入神经元过多吗?前馈神经网络不能识别字符吗?

推荐答案

对于手写字符识别，您需要

For handwritten character recognition you need

1. 许多训练示例(也许您应该使训练集失真)
2. 输出层中的softmax激活功能
3. 交叉熵误差函数
4. 进行随机梯度下降训练
5. 每层都有偏差

1. many training examples (maybe you should create distortions of your training set)
2. softmax activation function in the output layer
3. cross entropy error function
4. training with stochastic gradient descent
5. a bias in each layer

一个好的测试问题是手写数字数据集 MNIST .以下是在此数据集上成功应用神经网络的论文:

A good test problem is the handwritten digit data set MNIST. Here are papers that successfully applied neural networks on this data set:

Y. LeCun，L.Bottou，Y.Bengio和P. Haffner:基于梯度的学习应用于文档识别， http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf

Y. LeCun, L. Bottou, Y. Bengio and P. Haffner: Gradient-Based Learning Applied to Document Recognition, http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf

Dan Claudiu Ciresan，Ueli Meier，Luca Maria Gambardella，Juergen Schmidhuber:手写数字识别的深层简单神经网络Excel， http://arxiv.org/abs/1003.0358

Dan Claudiu Ciresan, Ueli Meier, Luca Maria Gambardella, Juergen Schmidhuber: Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition, http://arxiv.org/abs/1003.0358

我训练了具有784-200-50-10架构的MLP，并在测试集上获得了> 96％的准确度.

I trained an MLP with 784-200-50-10 architecture and got >96% accuracy on the test set.

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