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采用神经网络方法进行手写数字识别，包括特征提取和识别-
Using the neural network method for handwritten numeral recognition, including feature extraction and recognition
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RECOGNITION OF HANDWRITTEN ARABIC AND LATIN DIGITS WITH ARTIFICIAL
NEURAL NETWORK
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基于BP神经网络的手写数字识别系统，基于Matlab开发，实现手写输入板功能，特征提取，模型训练，手写识别等功能。详细使用方法在readme说明文档中。-Handwritten numeral recognition system based on BP neural network, developed based on Matlab, handwriting input board, feature extraction, model training, handwriting recogn
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BP神经网络，是运用人工智能的方法，训练权值，建立模式识别的模型。本程序源码，使用三层神经网络，，对隐层的节点个数使用经验公式，对其他的参数设置也进行了优化，实现了手写数字的识别， 可直接使用。
-BP neural network, the use of artificial intelligence methods, training weights to establish the model of pattern recognition. The program source cod
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手写体数字识别的Visual C实现使用神经网络算算法对手写体数字进行识别，训练后识别率可达90％左右。
-Handwritten numeral recognition, Visual C Neural Network Algorithm for recognition of handwritten digit recognition rate after training up to about 90 .
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基于BP网络的手写大写字母识别，能较好识别大写字母-Handwritten letter recognition based on BP neural network, to better identify the capital letters
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VC++实现多种方法的手写数字识别 可直接运行 如BP神经网络等 ，经测试可直接使用。
-Handwritten numeral recognition VC++ to achieve a variety of methods can be run directly such as BP neural network has been tested and can be used directly.
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基于BP神经网络的手写体数字识别中用到的特征提取工具。-Extraction tool based on the characteristics used in the BP neural network handwritten digit recognition.
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在VC++6.0环境下运行.手写数字的分类识别，并有详细的程序使用方法。分别采用模板匹配分类器，Bayes分类器，线性函数分类法，非线性分类，神经网络分类器进行识别，功能强大，还可以对比各种算法的优劣。-VC++6.0 environment run. Classification and recognition of handwritten digits, and a detailed program to use. Template matching classifier Bayes cla
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采用C++语言编写的基于BP神经网络的手写体数字识别程序，使用方式为通过命令提示符向main函数传递待识别的图像的绝对路径。-Using C++ language through the command prompt is passed to the main function to be identified image handwritten digit recognition based on BP neural network program, use absolute paths.
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基于神经网络的手写体数字识别，它是用matlab实现的，其中用3种不同的神经的网络方法实现了手写体数字的识别，非常利于初学者的学习和交流。-Handwritten digit recognition based on neural networks, which is achieved using matlab, in which three different neural network to achieve the recognition of handwritten digits, is
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手写数字的识别的matlab实现，运用基本的图像处理和BP神经网络来实现，程序的使用方法请参阅 readme.txt-Handwritten digit recognition matlab realize, the use of basic image processing and BP neural network to achieve the program' s usage, please refer to the readme.txt
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采用典型的BP算法实现了基于MNIST的手写数字识别采用输入层、隐含层和输出层的三层结构，实现了BP算法下的神经网络。用7000个样本进行自洽检验，正确率99.79 。-Using typical BP algorithm based on MNIST handwritten numeral recognition using input layer, hidden layer and output layer, three-layer structure, to achieve the neu
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This project provides matlab class for implementation of convolutional neural networks. This networks was developed by Yann LeCun and have sucessfully used in many practical applications, such as handwritten digits recognition, face detection, robot
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MFC实现手写数字的识别，包含Beyes分类，线性函数分类，神经网络分类等各种算法的实现-MFC to achieve recognition of handwritten digits, including Beyes classification to achieve a linear function of classification, neural network classification of various algorithms
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较实用的识别数字符号的实例，神经网络训练，识别手写数字-Identification of a practical instance of digital symbol, neural network training and recognition of handwritten Numbers
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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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实现数字识别,径向基神经网络手写体数字的识别准确率高-Digital recognition, RBF neural network handwritten digital recognition accuracy is high
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1、输入80乘80的英文手写字母图像，对训练数据进行训练，然后输入测试数据，完成字母识别。
2、txt文档详细说明如何配置、运行，一般情况下可以直接运行成功。
3、主要原理采用神经网络进行手写字母识别，准确率较高。
-1, enter 80 x 80 handwritten English alphabet image, the training data for training, and then enter the test data, complete the letter
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这个程序旨在把卷积神经网络算法应用于手写字符识别。程序有几种结构的神经网络可以通过比较不同结构而得到对识别率的影响。-This program is designed to put the convolutional neural network algorithm is applied to the handwritten character recognition. The structure of the program there are several kinds of neural
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