Description: 本文提出了一种新的车辆许可证盘子识别,并在此基础上提出了一种自适应图像分割方法-In this paper, a new algorithm for vehicle license
plate identification is proposed, on the basis of a novel adaptive
image segmentation technique (Sliding Windows) in
conjunction with a character recognition Neural Network. The
algorithm was tested with 2820 natural scene gray level vehicle
images of different backgrounds and ambient illumination.
The camera focused on the plate, while the angle of view and
the distance from the vehicle varied according to the
experimental setup. The license plates properly segmented
were 2719 over 2820 input images (96.4 ). The Optical
Character Recognition (OCR) system is a two layer
Probabilistic Neural Network with topology 108-180-36, whose
performance reached 97.4 . The PNN was trained to identify
multi-font alphanumeric characters from car license plates
based on data obtained from algorithmic image processing. Platform: |
Size: 831488 |
Author:keithe |
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Description: 在草图符号的自适应学习中，不同用户的训练样本数量可能不同，保持在不同样本数量下良好的学习效
果成为需要解决的一个重要问题．提出一种自适应的草图符号识别方法，该方法采用与训练样本个数相关的分类
器组合策略将模板匹配方法和SVM统计分类方法进行了高效组合．它通过利用支持小样本学习的模板匹配方法
和支持大量样本学习的SVM 方法，并同时利用草图符号中的在线信息和离线信息，实现了不同样本个数下自适应
的符号学习和识别．基于该方法，文中设计并实现了支持自适应识别的草图符号组件．最后，利用扩展的PIBG
Toolkit开发出原型系统IdeaNote．评估表明，该方法可以在24类草图符号分别使用1到2O个训练样本时具有较
高的识别正确率和较好的时间性能．-In the sketch symbol adaptive learning, the number of training samples of different users may be different, to keep the number of samples under different good learning effect
Fruit become an important issue to be resolved. The draft plan proposed an adaptive character recognition method using the number associated with the classification of training samples
Combination strategy will be the template matching method and SVM classification method was efficient statistical combination. It supports a small sample study by using a template matching method
And support a large number of samples to learn the SVM method, and sketch symbols while using online information and offline information and achieve a number of different samples of adaptive
Learning and recognition of symbols. Based on this method, the paper designed and implemented to support adaptive sketch recognition symbol components. Finally, using the extended PIBG
Toolkit developed a prototype system IdeaNote. Is shown th Platform: |
Size: 626688 |
Author:郭事业 |
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Description: 关于灰度图像二值化的算法的详解，针对各个领域的应用，汉字识别，摄像采集，政府资源等等，适合初学者使用，灰度图像-On the gray image binarization algorithm Wapakhabulo, for applications in various fields, Chinese character recognition, video capture, government resources, etc., suitable for beginners, gray image Platform: |
Size: 3084288 |
Author:月平 |
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Description: Principal Component Analysis. Very important for pattern recognition(ie. optical character recognition) A great fundamental file for the beginner. Even those who doesn t know what is variance can start learning about OCR basics from this pdf.-Principal Component Analysis. Very important for pattern recognition(ie. optical character recognition) A great fundamental file for the beginner. Even those who doesn t know what is variance can start learning about OCR basics from this pdf. Platform: |
Size: 91136 |
Author:Adil |
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Description: 一个关于车牌识别的完整程序，字符识别部分用的是神经网络的相关知识-A complete program of license plate recognition, character recognition part of the neural network with the knowledge Platform: |
Size: 545792 |
Author:晨菁 |
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Description: 实现对印刷体汉字的识别，在识别过程中，主要提取了5个特征：笔画特征，连通域个数，穿线特征，投影特征和粗外围特征。-Implementation of the printed character recognition, the recognition process, the main extracted five characteristics: stroke characteristics, the number of connected components, threading features, the external projection features and characteristics of crude. Platform: |
Size: 594944 |
Author:张燕 |
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Description: Optical Character Recognition (OCR) systems have been effectively
developed for the recognition of printed characters of non-Indian languages. Efforts
are on the way for the development of efficient OCR systems for Indian languages,
especially for Kannada, a popular South Indian language.We present in this paper
an OCR system developed for the recognition of basic characters (vowels and consonants)
in printed Kannada text, which can handle different font sizes and font
types. Platform: |
Size: 217088 |
Author:avi |
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Description: 印刷体汉字识别，很好用，没断程序都有说明。-Printed Chinese character recognition, useful, no broken processes have been described. Platform: |
Size: 5029888 |
Author:xuhaixia |
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Description: 基于BP神经网络的脱机手写汉字识别研究，包含预处理、汉字识别、后处理和识别输出-The Research of Off-line hand written Chinese character recognition Based on BP neutral network Platform: |
Size: 3180544 |
Author:唐笑 |
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Description: 绝对好的！用matlab实现的车牌识别程序，包含字符提取和字符切割模块的程序和字符模板-Absolutely good! License plate recognition using matlab implementation procedures, including character extraction and character segmentation module of the program and character templates Platform: |
Size: 252928 |
Author:小安 |
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Description: 包括手写文字识别的各个步骤，图像的预处理、裁剪、大小转换、人工神经网络训练和识别等等。-Handwritten character recognition, including the various steps, image preprocessing, cropping, size, conversion, training and recognition of artificial neural networks and so on. Platform: |
Size: 155648 |
Author:木易 |
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Description: 光学字符识别（即OCR）技术。给出了一些基于linux/KDE的英文OCR代码，该代码对于如何实现一个OCR系统有很好的启发性。-Optical character recognition (ie OCR) technology. Gives some based on linux/KDE English OCR code, the code for how to achieve an OCR system is a good instructive. Platform: |
Size: 846848 |
Author:sylvia |
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Description: 介绍了一种基于神经网络的文字识别系统，给出了详细的实现过程以及结果-Introduced a neural network based character recognition system, gives a detailed implementation process and results Platform: |
Size: 210944 |
Author:林 |
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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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