搜索资源列表
手写识别系统
- 数字识别,识别手写体。 使用神经网络算法编写。 识别率一般,带训练功能-Number recognizing system,used to recogize the hand writing figures.It is writen by nero network and has low recognizing effects but we can exercise it.
手写体数字的识别程序
- 运用神经网络 算法所写的手写数字的识别程序-networks using neural network algorithm written in the handwriting digit identification procedures
模式识别的一些预处理
- 模式识别的一些预处理,包括:图像压缩的例子:行程编码算法RCL, 手写体数据变换成像素位图的算法。-some pretreatment pattern recognition, including : Image Compression examples : RCL itinerary coding algorithm, handwritten data transformation into pixel bitmap algorithm.
脱机手写体汉字识别演示程序 v1.1
- 脱机手写汉字识别-Chinese Character Recognition
PRAssign
- 脱机手写体识别Matlab源程序 包括特征提取、bayes分类器、K近邻分类及最近邻分类。 Testscr iptRecognition.m:测试代码 scr iptFeaExtract.m :特征提取 KNearestEstimate.m :K近邻估计 NearestEstimate.m : 最近邻估计 BayesTrain.m :训练bayes分类器 Bayes.m :测试bayes分类器 CrossValidate.m :m交叉验证 -Offlin
HandWrite
- 手写体识别,基于笔画,对笔画提取特诊点来匹配。
bp_test
- 利用人工智能,神经网络中的BP网络实现的,可以有效识别手写体数字-Using artificial intelligence, neural network BP network, and can effectively identify the handwritten numeral
scriptnumber
- 手写体数字的预处理、特征提取以及识别,功能较为全面-Handwritten digit preprocessing, feature extraction and recognition, function more comprehensive
libsvm
- 基于libsvm的手写体识别 内附程序使用说明以及测试文件-Libsvm-based handwriting recognition included the program' s instructions and test files
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数字识别,利用概率神经网络识别1-9的手写数字,matlab程序(Handwritten numeral recognition based on probabilistic neural network)
手写体数字识别系统
- 用神经网络准确实现手写体数字的识别,很实用(Neural network to achieve handwritten numeral recognition, very practical)
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)
手写体字符识别
- 简单的手写体字符识别,利用了k近邻和支持向量机算法(Simple handwritten character recognition, using the k nearest neighbor and support vector machine algorithm)
英文字母识别
- 基于BP网络的手写体大写字母识别,相对入门(Handwritten Capital Letter Recognition Based on BP Network)
bayes和神经网络的手写体数字识别程序(matlab)
- bayes和神经网络的手写体数字识别程序(matlab)(bayes and neural network handwritten numeral recognition program (matlab))
bpnn
- 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)
LeNet
- tensorflow实现手写体识别(包含mnist数据集)(Handwritten recognition by tensorflow)
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,可用于课程设计(Handwritten digit recognition based on probabilistic neural network can be used for course design)
imgClassifier - 副本
- TensorFlow2.0进行Minist手写体识别(Minist handwriting recognition by TensorFlow2.0)
手写体识别/python
- 此程序使用的是python语言,能对手写数字0~9进行识别。