Description: 基于神经网络的手写数字识别的源代码，绝对能够正常编译并运行！-based on neural network handwritten numeral recognition of the source code is absolutely normal to compile and run! Platform: |
Size: 207872 |
Author:田巾 |
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Description: 在matlab平台下开发的对手写数字识别代码，-In matlab platform developed under the code of handwritten numeral recognition, Platform: |
Size: 1024 |
Author:解梅 |
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Description: 用PCA进行手写数字识别，主要是用Matlab实现了原始图像的预处理，进而分别采用传统的PCA方法、改进的PCA算法和2维PCA算法进行了数字识别。-PCA carried out using handwritten numeral recognition, is mainly used Matlab realize the original image preprocessing, and then using the traditional PCA methods, improved PCA algorithm and 2-D PCA algorithm of digital identification. Platform: |
Size: 8192 |
Author:xulei |
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Description: 本文论述并设计实现了一个脱机自由手写体数字识别系统。文中首先对待识别数字的预处理进行了介绍，包括二值化、平滑滤波、规范化、细化等图像处理方法；其次，探讨了如何提取数字字符的结构特征和笔划特征，并详细地描述了知识库的构造方法；最后采用了以知识库为基础的模板匹配识别方法,并以MATLAB作为编程工具实现了具有友好的图形用户界面的自由手写体数字识别系统。实验结果表明，本方法具有较高的识别率，并具有较好的抗噪性能-In this paper, and designed to achieve a free offline handwritten numeral recognition system. First, the identification number of the preconditioning treatment were introduced, including binarization, smoothing filtering, standardization, refinement, such as image processing method Secondly, to explore how to extract the number of character strokes of structural features and characteristics, and described in detail Knowledge of the construction method Finally the use of a Knowledge-based template matching method to identify and MATLAB as a programming tool to achieve with a friendly graphical user interface of free handwritten numeral recognition system. The experimental results show that this method has higher recognition rate, and has better anti-noise performance Platform: |
Size: 1224704 |
Author:wangjinfeng |
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Description: 本文论述并设计实现了一个脱机自由手写体数字识别系统。文中首先对待识别数字的预处理进行了介绍，包括二值化、平滑滤波、规范化、细化等图像处理方法；其次，探讨了如何提取数字字符的结构特征和笔划特征，并详细地描述了知识库的构造方法；最后采用了以知识库为基础的模板匹配识别方法,并以MATLAB作为编程工具实现了具有友好的图形用户界面的自由手写体数字识别系统。实验结果表明，本方法具有较高的识别率，并具有较好的抗噪性能。-In this paper, and design implementation of a freely Offline handwritten numeral recognition system. First, identify the figure of the pre-processing treatment were introduced, including binarization, smoothing filtering, standardization, refinement, such as image processing method Secondly, to explore how to extract the structural features of characters and stroke characteristics, and described in detail Knowledge of the construction method Finally the use of a Knowledge-based template matching to identify methods and MATLAB as a programming tool to achieve with a friendly graphical user interface of freely handwritten numeral recognition system. The experimental results show that this method has higher recognition rate, and has better anti-noise performance. Platform: |
Size: 349184 |
Author:kongchao |
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Description: 本文论述并设计实现了一个脱机自由手写体数字识别系统。文中首先对待识别数字的预处理进行了介绍，包括二值化、平滑滤波、规范化、细化等图像处理方法；其次，探讨了如何提取数字字符的结构特征和笔划特征，并详细地描述了知识库的构造方法；最后采用了以知识库为基础的模板匹配识别方法,并以MATLAB作为编程工具实现了具有友好的图形用户界面的自由手写体数字识别系统。实验结果表明，本方法具有较高的识别率，并具有较好的抗噪性能。-In this paper, designed and implemented an off-line handwritten numeral recognition system. The paper first pre-treatment identification numbers were introduced, including binarization, smoothing filter, normalization, thinning and other image processing methods secondly, to explore how to extract the number of characters in the structural characteristics and stroke features, and described in detail Knowledge of construction methods finally adopted in order to Knowledge-based template matching recognition method, and to MATLAB as a programming tool to achieve a friendly graphical user interface handwritten numeral recognition system. The experimental results show that this method has higher recognition rate, and has good anti-noise performance. Platform: |
Size: 350208 |
Author:zhangying |
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Description: 支持向量机的研究现已成为机器学习领域中的研究热点，其理论基础是Vapnik[3]等提出的统计学习理论。统计学习理论采用结构风险最小化准则，在最小化样本点误差的同时，缩小模型泛化误差的上界，即最小化模型的结构风险，从而提高了模型的泛化能力，这一优点在小样本学习中更为突出。SVM理论正是在这一基础上发展而来的，经过十几年的研究和发展，已开始逐步应用于一些领域。在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势，已经在模式识别、函数逼近和概率密度估计等方面取得了良好的效果。- Support Vector Machine (SVM) is a new machine learning technique in recent years developed based on statistical learning theory (SLT). It wins popularity due to many attractive features and emphatically performance in the fields of nonlinear and high dimensional pattern recognition. The theory and algorithm of SVC is studied at first, then, simulation is to recognize handwritten numeral with the Lib-SVM toolbox. At last, we study the result, which shows that the SVC can do the classification problem with good performance, shorter operation time and is more suitable for real-time implementation. Platform: |
Size: 1155072 |
Author:任修齐 |
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Description: 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 recognition and other functions. Detailed instructions in the readme document. Platform: |
Size: 197632 |
Author:yang |
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Description: 《MATLAB神经网络原理与实例精解》中chap13的例子 基于概率神经网络的手写体数字识别-" MATLAB network principles and examples of fine nerve Solutions" in the example chap13- Based Probabilistic Neural Network handwritten numeral recognition Platform: |
Size: 187392 |
Author:miaozhiwei |
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Search - numeral recognition in matlab