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Paper2
- An Efficient Feature Extraction Algorithm for the Recognition of Handwritten Arabic Digits
BP
- 用BP神经网络进行手写数字的识别,特征提取是基于统计模式识别的方法-BP neural network with the handwritten numeral recognition, feature extraction is based on statistical pattern recognition approach
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
multihandwrittenandfeatureextraction
- 基于多特征提取的手写汉字识别算法及其实现.-Feature extraction based on multi handwritten Chinese character recognition algorithm and its implementation.
NumberRecognition
- 简单的手写数字识别，通过二值化，提取特征值，投影法匹配来实现-Simple handwritten numeral recognition, through the binarization, feature extraction value, projection matching to achieve
handwrite-number-recognition
- 手写数字识别是“光学字符识别技术”（简称OCR）的一个分支，它研究的对象是：如何利用电子计算机自动辨认人手写在纸张上的阿拉伯数字。它包含样本图片，可以对图片进行预处理，特征提取，还可以给出网络训练的误差指数曲线图-How to use electronic computer automatically recognized people handwritten on paper Arabic numerals.It contains sample pictures, pictures can
te
- 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
handwritingPrecognitionPGUI
- 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
shouxieshuzi
- 里面包含了手写数字识别代码，有PCA特征提取，FSVM分类器识别，是很好的学习资源-Which contains a handwritten digital identification code, PCA feature extraction, FSVM classifiers recognition, is a good learning resource
PCAFex
- PCA feature extraction in handwritten digit recognition.
Handwritten-Recognition-using-Neural-Networks-Bas
- Handwritten Recognition using Neural Networks Based on Multiple Feature Extraction Algorithms
SVM-handwritten-digits-recognitio
- 介绍了在提取穿越次数特征、粗网格特征以及密度特征提取的基础上应用SVM进行手写体阿拉伯数字识别的方法。-Introduced the extraction across a number of features, coarse grid and density feature extraction on the basis of the application of SVM method for handwritten digits recognition.
OCR-ResultaProgram
- An algorithm to recognize distorted Machine and handwritten characters is proposed. It uses a feature point extraction-based recognition approach. A new verification scheme, which deals with this problem, is presented. A new feature extractor set is
handwritten-numeral-recognition
- 本案例描述了图像中手写阿拉伯数字的识别过程，对手写数字识别的基于统计的方法进行简要介绍和分析，并通过开发一个小型的手写体数字识别系统来进行实验。手写体数字识别系统需要实现手写数字图像的读取功能、特征提取功能、数字的模板特征库的建立功能及识别功能-This case describes the image recognition process handwritten Arabic numerals, a brief descr iption and analysis of the handwri
shuangtanxingwangluo
- 特征提取是手写体汉字识别的关键，目前四方向网格特征已被实验证实是一种较好的手写体汉字特征。 针对通常的纵横弹性网格对汉字“撇、捺”笔画特征提取的不足．提出一种新的网格构造技术——对角弹性网格，它由45度和135度的对角直线构成，将汉字图像划分为多个菱形，能够很好地适应汉字在“撇、捺”方向的变化。将这两种网格单独，以及相互组合成双网格等情况分别进行手写体识别实验，实验结果验证了对角弹性网格的有效性和双弹性网格的高识别率性。 -Feature extraction is the key to
8fangx
- 在联机手写中文识别中一种针对8方向特征提取的改进算法(In online handwritten Chinese character recognition, an improved algorithm for feature extraction in 8 directions is proposed)
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