搜索资源列表
t10k-labels-idx1-ubyte
- 用于手写数字识别的预测数据(标签) 数据格式:前32位为2049,再32位为数据数量,之后每一位都是标签值(Predictive data (tags) for handwritten numeral recognition)
train-images-idx3-ubyte
- 用于手写数字识别的训练数据(图片) 数据格式:前32位为2049,再32位为数据数量,再32位为图片宽度M,再32位为图片高度N,之后每N*M位都是图片的像素值(Training data (pictures) for handwritten digit recognition)
t10k-images-idx3-ubyte
- 用于手写数字识别的预测数据(图片) 数据格式:前32位为2049,再32位为数据数量,再32位为图片宽度M,再32位为图片高度N,之后每N*M位都是图片的像素值(Predictive data (pictures) for handwritten numeral recognition)
数字识别
- 对于自己手写的数字进行识别,效果比较不错,准确率在八成以上(recognition of handwriting numbers, with very good testing results, can successfully recognize 80 percent)
数字识别
- 手写体识别,包括样本,基于传统神经网络编写,使用MATLAB神经网络工具箱(Handwriting Recognition)
CNN2-数字识别
- 利用C写的一个用卷积神经网络做数字识别程序(Using C to write a convolution neural network to do digital identification procedures)
kNN
- KNN算法改进约会网站配对效果;KNN实现手写数字识别(KNN algorithm to improve the matching effect of dating sites; KNN handwritten numeral recognition)
手写体数字识别
- 可以识别手写体数字,识别率在百分之90以上。贝叶斯决策论(Handwritten numerals can be recognized)
DigitReg
- 此程序用于各种图片中的数字识别,基于VS2013+OPENCV2.4.9,简单好用(This program is used for digital identification of various pictures, based on VS2013+OPENCV2.4.9, simple and easy to use)
BP神经网络实现手写数字识别matlab实现
- BP神经网络实现手写数字识别matlab实现(Matlab implementation of handwritten digit recognition based on BP neural network)
手写数字识别
- 一个练习机器学习的算法,解决手写数字识别的算法(An algorithm that exercises machine learning to solve the handwritten numeral recognition algorithm)
mnist1
- 训练手写数字识别算法,正确率达到91.6%(Training handwritten numeral recognition)
BP神经网络手写数字识别
- 使用bp神经网络算法识别手写阿拉伯数字图像,三层的误差反馈神经网络,可输出准确率,数据集为60000条数据,每条数据是一张28*28的图片(The BP neural network algorithm is used to recognize handwritten Arabia digital images, and the error feedback neural network of three layers can output the accuracy rate. The data
Captcha-Recognizer-master
- 简单数字识别系统用于数字的识别 bin obj Properties FrmMain.cs FrmMain.Designer.cs FrmMain.resx GetCode.cs GetCode.csproj GetCode.csproj.user(bin obj Properties FrmMain.cs FrmMain.Designer.cs FrmMain.resx GetCode.cs GetCode.csproj GetCode.csproj.user
mnist
- 深度学习时间手写数字识别,使用python和tensorflow实现(Handwritten numerals recognition in depth learning time)
数字识别4整理
- 该程序是基于opencv的 可以识别记事本的数字并输出识别结果 在opencv2.4.9,vs2013下能够运行(The program is a opencv - based number that can identify notepad and output recognition results that can run under opencv2.4.9, vs2013)
C# OCR光学识别,数字识别率达100%
- Ocr光学识别数字,识别率达百分百,基于AspriseOcr(AspriseOcr removal of projectile frame restriction)
text1
- 用SVM进行数字识别,不过成功率不是特别高(Using SVM for digital recognition)
Classify handwriten digits
- python下CNN手写数字识别CNN Classify handwriten digits(CNN Classify handwriten digits)
基于概率神经网络的手写体数字识别
- 基于概率神经网络的手写体数字识别Matlab程序(Handwritten digital recognition Matlab program based on probabilistic neural network)