搜索资源列表
stm32手写识别实验
- 可以利用stm32的彩屏进行手写文字的输入,方便快捷。(Can be handwritten text input using the STM32 color screen, convenient and quick.)
handwriting recognition GUI
- 本文主要实现手写数字识别,利用多类逻辑回归与神经网络两种方法实现,并编写有GUI界面。(This paper mainly implements handwritten numeral recognition, using multiple logic regression and neural network to achieve two methods, and the preparation of a GUI interface.)
手写识别实验
- stm32f4手写设置,运行与测试等,开发平台KEIL,希望对大家有帮助(Stm32f4 handwriting settings, running and testing, development platform KEIL, we want to help)
neuralnetwork-sample
- 由java编写的,具有gui界面的,手写数字识别神经网络示例(Written by Java, with GUI interface, handwritten numeral recognition neural network examples)
train-labels-idx1-ubyte
- 用于手写数字识别的训练数据(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(Training data (tags) for handwritten digit recognition)
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)
Character_Recognition
- 本程序主要参照论文,《基于OpenCV的脱机手写字符识别技术》实现了,对于手写阿拉伯数字的识别工作。识别工作分为三大步骤:预处理,特征提取,分类识别。预处理过程主要找到图像的ROI部分子图像并进行大小的归一化处理,特征提取将图像转化为特征向量,分类识别采用k-近邻分类方法进行分类处理,最后根据分类结果完成识别工作。 程序采用Microsoft Visual Studio 2010与OpenCV2.4.4在Windows 7-64位旗舰版系统下开发完成。并在Windows xp-32位系统下测试
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)
chapter1
- 识别数字,实现手写数字的自动识别,节省人力(recognizeRecognition of numbers, automatic recognition of handwritten numerals, and manpower saving)
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)
handwrite2
- 采用KNN算法,用PYTHON语言实现的手写数字图像识别(Using KNN algorithm, handwritten digital image recognition with PYTHON language)
数字识别系统v2
- 可以手写输入数字 但是要2014b以前的MATLAB才能运行(You can write the numbers by hand, but you can run them with the MATLAB 2014b)
MNIST
- 简单的手写数字识别,在深度神经网络中的简单尝试,对于初学者有个很好的理解(Simple handwritten numeral recognition, in the depth of neural network simple attempt, for beginners have a good understanding)
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)
手写体数字识别界面程序
- 模糊模式识别,贝叶斯,手写识别。用于识别手写的数字。有样本图片。(Fuzzy pattern recognition, Bayes, handwriting recognition. Used to recognize handwritten numbers.)
test1
- 需要tesseract ocr api支持,测试用手写识别(Tesseract OCR API support is required for handwriting recognition test)
mnist1
- 训练手写数字识别算法,正确率达到91.6%(Training handwritten numeral recognition)