Description: This an digit recognition application. (OCR or ICR application). First you draw a digit in the picturebox. Then the image processing begins and recognize the digit and returns you the result. I used correlation matching algorithm for character recognition and k-neighbor classifying algorithm to select the true digit. Database includes handwritten digit examples which are collected from a hundred persons. These digit images converted to binary type before added to the database.-This an digit recognition application. (OCR or ICR application). First you draw a digit in the picturebox. Then the image processing be Huggins and recognize the digit and returns you the result. I used correlation matching algorithm for character recognition and k-neighbor clas sifying algorithm to select the true digit. Dat abase includes handwritten digit examples whi ch are collected from a hundred persons. These d igit images converted to binary type before add ed to the database. Platform: |
Size: 41984 |
Author:Toby |
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Description: 手写体数字识别的VC实现
使用神经网络算法对手写体数字进行识别，训练后识别率可达90％左右。-Handwritten Digit Recognition of the VC to achieve the use of neural network algorithm for handwritten numeral recognition, training, recognition rate can reach about 90. Platform: |
Size: 210944 |
Author:lissdd |
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Description: 本实例实现了手写数字的识别，采用联机字符识别技术，提供了1， 2 ，3 ，7， 4 几个数字的识别-The implementation of the examples of handwritten digit recognition, the use of online character recognition technology to provide the 1，2,3，7，4 figure some of the identification Platform: |
Size: 34816 |
Author:薛仁 |
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Description: 应用matlab程序，实现了对手写数字的识别，准确率非常高。-Matlab application procedures, to achieve the hand-written digit recognition, the accuracy rate is very high. Platform: |
Size: 196608 |
Author:吕晋普 |
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Description: 基于Fisher判别器的的手写数字识别，对其进行了VC++的实现！-Based on Fisher Discriminant device Handwritten Digit Recognition and to carry out the implementation of the VC++! Platform: |
Size: 8646656 |
Author:蔡锋 |
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Description: Handwriting Recognition using Kernel Discriminant Analysis.
Demonstration of handwritten digit recognition using Kernel Discriminant Analysis and the Optical Recognition of Handwritten Digits Data Set from the UCI Machine Learning Repository. Platform: |
Size: 1415168 |
Author:reyjav |
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Description: 神经网络手写数字识别。配合美国MNIST标准手写数字字体库-Handwritten digit recognition neural network. With the U.S. standard of handwritten digital font library MNIST Platform: |
Size: 4096 |
Author:li |
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Description: 介绍数字图像统计模式识别、模式识别决策方法及实现：测试代码有人脸检测与特征点定位、汽车牌照识别、脑部医学影像诊断、印刷体汉字识别、手写体数字识别、运动图像分析，共6个数字图像模式识别应用实例-Introduction of digital image statistical pattern recognition, pattern recognition and realization of decision making: the test code was face detection and feature points, car license plate recognition, medical imaging diagnosis of the brain, printed Chinese character recognition, handwritten digit recognition, motion image analysis, a total of 6 Application of digital image pattern recognition Platform: |
Size: 13958144 |
Author: |
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Description: 根据数字的笔画和书写顺序两个属性开发的手写数字识别系统-According to the numbers of strokes and writing order two properties developed by handwritten numeral recognition system Platform: |
Size: 386048 |
Author:zcf |
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Description: 手写字符由于书写风格和习惯的不同，造成字符模式的不稳定。针对这一问题，本文首先对字符图像进行图像
预处理，统一字符笔画的粗细，改善局部特征，随后利用二维主分量分析法（2DPCA）直接对字符图像矩阵进行变换，
抽取字符特征，建立字符的特征矩阵及重构模型，利用最邻近方法和重构误差法进行字符识别-Since handwritten characters of different writing styles and habits, resulting in character-mode instability. To solve this problem, this paper first character images for image pre-processing, unified character stroke thickness, improve local characteristics, followed by the use of two-dimensional principal component analysis (2DPCA) directly on the character image matrix transform to extract character features, build character characteristic matrix and reconstruction model, using the nearest neighbor method and the reconstruction error method for character recognition Platform: |
Size: 909312 |
Author:xj |
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Description: 手写体数字识别有着重大的使用价值,用多层BP 网络来识别手写体数字是手写体数字识别的一大进步,但是,用
单纯的BP 网络来识别也存在识别精度不高等的问题。将BP 网络技术和数字本身的结构特征结合起来,提出了一种基于
结构特征分类BP 网络的手写体数字识别新方法-Handwritten numeral recognition has great value in use, multi-layer BP neural network to recognize handwritten numerals handwritten numeral recognition is a big step forward, but with a simple BP network to identify the recognition accuracy is not high, there are problems. The BP neural network technology and digital combine their structural characteristics, we propose a classification based on BP network structure Handwritten Digit Recognition Method Platform: |
Size: 244736 |
Author:xj |
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