搜索资源列表
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)
数字识别
- 人工智能深度学习，基于matlab的数字识别程序(Artificial intelligence deep learning, matlab based digital recognition program)
least_square
- 利用最小二乘法实现手写体数字识别，采用mnist数据集(simple program using least-square)
MATLAB 神经网络用于数字识别源程序
- 使用matlab神经网络工具包进行图像数字识别进行(image digital recognition using matlab neural network toolkit)
BP神经网络数字识别（matlab2014a）
- 利用BP神经网络识别易拉罐底字符识别，运行前注意阅读算法说明和代码中相对路径的设置。(The BP neural network is used to identify the character recognition of the bottom of the can, and the pre operation attention to the reading algorithm and the setting of the relative path in the code.)
digit_recognizer[1]
- python SVM(支持向量机)实现手写数字识别(SVM realization of digital recognition)
BPnetwork_code
- 手写体数字识别是模式识别中一个非常重要和活跃的研究领域，数字识别也不是一项孤立的技术，它所涉及的问题是模式识别的其他领域都无法回避的；应用上，作为一种信息处理手段，字符识别有广阔的应用背景和巨大的市场需求。因此，对数字识别的研究具有理论和应用的双重意义。(Handwritten numeral recognition is a very important and active research field in pattern recognition, digital recognition
matlab
- 基于BP神经网络手写数字识别，main函数是主函数，识别的成功率在86%左右。(Based on the BP Neural Network handwritten digital recognition, the main function is the main function, and the recognition success rate is about 86%.)
Hand_num
- 基于神经网络及GUI触摸板的手写数字识别，基本的机器学习例子(Handwritten numerals recognition based on neural network and GUI touch board, basic machine learning examples)
tensorboard
- tensorflow手写数字识别，提高识别的准确率(Tensorflow handwritten numeral recognition, improve the accuracy of recognition.)
cnn
- 手写数字识别的简单实现，CNN入门到深入，三个版本供读者使用。(Simple Implementation of Handwritten Number Recognition, Getting Started with CNN)