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基于人工神经网络的手写识别系统,具体没怎么实现过-Artificial neural network based handwriting recognition system, specifically not realized how
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c#开发的基于BP神经网络的手写体识别,代码结构清晰,是学习c#和BP神经网络的好资料-C# development of handwriting recognition based on BP neural network, the code structure is clear, is good information to learn c# and the BP neural network
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闲时无聊,搭了一个基于深度神经网络的手写数字识别系统。该系统在手写数字数据库mnist测试达到了99.22 的准确率。整个系统基于C++开发,可以很方便的移植到其他平台。
其中手写数字数据库mnist(http://yann.lecun.com/exdb/mnist/),有60000个训练样本数据集和10000个测试用例。它是由Google实验室的Corinna Cortes和纽约大学柯朗研究所的Yann LeCun建立的一个手写数字数据库。同时它是nist数据库的一个子集。
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cnn卷积神经网络用于手写数字识别,cnet_tool是一个demo-cnn convolution neural network for handwriting recognition, cnet_tool is a demo
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一个基于BP神经网络的matlab手写识别程序,可识别0-9的数字,如果加点英文字母的图片,再改改参数,应该能识别英文。-Based on BP neural network matlab handwriting recognition program that identifies 0-9, if the English alphabet picture plus point, and then changed the parameters, should be able to identif
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这是一个离线手写识别程序。应用BP神经网络算法。-This is an off-line handwriting recognition program. BP neural network algorithm.
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利用RBM神经网络实现了手写数字体识别的GUI程序-RBM neural network using several fonts handwriting recognition GUI program
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通过学习BP神经网络技术,对手写数字进行识别,基于结构的识别法及模板匹配法来提高识别率。-Through the study of BP neural network technology, digital handwriting identification, structure-based identification method and template matching method to improve the recognition rate.
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这是个卷积神经网络的实现代码,对手写体进行识别,现在正确率可以达到90 -This is a convolution neural network implementation code of conduct handwriting recognition accuracy rate is now 90
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RNNLIB可以用来做语音识别、手写字符识别。由大牛Alex Graves编写,专门做RNN、LSTM的研究。他的主页是http://www.cs.toronto.edu/~graves/-RNNLIB is a recurrent neural network library for sequence labelling
problems, such as speech and handwriting recognition.
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使用MATLAB实现bp神经网络实现手写数字图片识别-Use bp neural network handwriting recognition digital pictures
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手写识别的实现,由bp算法和梯度下降算法实现的神经网络-Handwriting recognition achieved by the bp algorithm and gradient descent algorithm neural network
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手写数码(0到9)识别的神经网络框架。附有数据训练集和测试集。采用随机梯度下降的BP算法。可以修改参数,加入drop out和动量法。-Digital handwriting (0 to 9) of the neural network recognition framework. With training and test data sets. BP algorithm using stochastic gradient descent. Parameters can be modified
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手写体识别_模板匹配识别方法,通过matlab实现基于神经网络的手写数字识别-Handwriting recognition _ template matching method, the matlab implementation of handwritten digit recognition based on Neural Network
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用Python3实现BP神经网络对MNIST数字手写体识别,下载就能用(Using Python3 to implement BP neural network for MNIST digital handwriting recognition, download can be used)
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反向传播算法与利用卷积神经网络识别手写体(Back propagation algorithm and recognition of handwriting by using convolution neural network)
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