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基于 Adaboost的手写体数字识别 总被引:5,自引:2,他引:3
提出了一种新的基于集成学习算法Adaboost的手写体数字识别系统。Adaboost方法可以在仅比随机预测略好的弱分类器基础上构建高精度的强分类器。实验证明,基于Adaboost的手写体数字识别系统具有较高的识别率和泛化能力,已经应用在OCR识别软件中。 相似文献
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手写体字符识别的多特征多分类器设计 总被引:4,自引:0,他引:4
特征选取和分类器设计是字符识别系统设计的关键。文章针对手写体汉字和阿拉伯数字混和字符集的识别提出了依据不同的分类要求,分别选取不同的字符特征并采用神经网络多分类器进行识别的设计方法。实验结果表明,该方法用于手写体混合字符集的识别是行之有效的。 相似文献
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本文提出了一种基于外接同心圆结构提取贯穿特征码的自由手写体数字的神经网络识别。该方法是用自由手写体数字的外接同心圆来提取其贯穿持征码,将获得的模式特征训练改进的BP神经网络分类器,从而达到快速分类的目的。将其应用于邮政编码识别系统,单字的识别率达到97%以上,整信的识别率可达到92%以上,得到了令人满意的结果。 相似文献
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吕淑静 《图象识别与自动化》2005,(2):39-42
本文根据孟加拉数字的特点,用Kirsch算子提取字符图像象素的水平、垂直、右对角线和左对角线特征矢量,采用BP神经网络作分类器进行识别。实验结果显示,对于孟加拉手写体数字具有较高的识别率和较快的识别速度,并对其它手写体数字也有很强的应用性。 相似文献
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基于Bagging的手写体数字识别系统 总被引:1,自引:0,他引:1
Bagging是一种用来提高学习算法准确度的方法,通过构造一系列预测函数并将其结果按投票规则进行合成,就可以将一个弱学习算法提升为强学习算法。本文针对UK测试量表中的手写体数字,设计并实现了一个以神经网络为弱分类器的、基于Bagging的手写体数字识别系统。与单个神经网络分类器相比,Bagging后的系统显示了更加优良的性能。 相似文献
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In this paper, we develop a new method to separate single-touching handwritten numeral strings with two numerals using structural features. A binary image of a single-touching handwritten numeral string is preprocessed with an efficient algorithm for smoothing, linearization and detection of structural points of image contours. The touching region of a single-touching handwritten numeral string is determined based on distribution of the structural points in the handwritten numeral string. A candidate touching point is preselected based on the geometrical information of a special structural point in the touching region. In some cases, the left or right lateral numeral of a single-touching handwritten numeral string can be recognized. The recognition information can be utilized to correct the position of the candidate touching point. We have tested our method on image samples taken from the U.S. National Institute of Science and Technology (NIST) database. We used 500 sample images for training and obtained a correct separation rate of 99.1%. For 3287 test samples not used for training the correct separation rate was 97.2%. 相似文献
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Hui Zhu X. L. Tang Peng Liu 《International Journal on Document Analysis and Recognition》2006,8(1):27-34
A hybrid model based on the combination of an orthogonal Gaussian mixture model (OGMM) and a multilayer perceptron (MLP) is proposed in this paper that is to be used for Chinese bank check machine printed numeral recognition. The combination of MLP with OGMM produces a hybrid model with high recognition accuracy as well as an excellent outlier rejection ability. Experimental results show that the proposed model can satisfy the requirements of Chinese bank check printed numeral recognition where high recognition accuracy, high processing speed, and high reliability are needed.
Correspondence to: Hui Zhu 相似文献
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小波神经网络在手写数字识别中研究与应用 总被引:1,自引:0,他引:1
岳耀雪 《电脑编程技巧与维护》2010,(14):85-87,97
针对手写数字识别的特点,讨论了数字识别预处理的方法,包括二值化、倾斜矫正、细化和归一化。利用小波函数代替传统神经网络中的激活函数,构建了用于数字识别,小波神经网络系统。仿真结果显示,新系统大大提高了网络训练速度,数字识别的正确率也明显提高。 相似文献
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Previous handwritten numeral recognition algorithms applied structural classification to extract geometric primitives that characterize each image, and then utilized artificial intelligence methods, like neural network or fuzzy memberships, to classify the images. We propose a handwritten numeral recognition methodology based on simplified structural classification, by using a much smaller set of primitive types, and fuzzy memberships. More specifically, based on three kinds of feature points, we first extract five kinds of primitive segments for each image. A fuzzy membership function is then used to estimate the likelihood of these primitives being close to the two vertical boundaries of the image. Finally, a tree-like classifier based on the extracted feature points, primitives and fuzzy memberships is applied to classify the numerals. With our system, handwritten numerals in NIST Special Database 19 are recognized with correct rate between 87.33% and 88.72%. 相似文献
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对手写表格数字识别系统进行研究,提出了输出规则的概念,解决了常用表格数据识别处理的输出描述问题.用此方法已成功地开发出手写数字表格识别处理系统,该系统具有运算量小、抗干扰性强、通用性好、识别率高等特点. 相似文献
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耿超 《计算机光盘软件与应用》2011,(17)
本文介绍了神经网络、数字识别的相关内容及其概念,在此基础上重点研究了基于神经网络的数字识别系统。本文对基于神经网络的数字识别系统包含的功能,以及每个功能模块所使用的技术进行了阐述。最后对基于神经网络的数字识别系统所具有的优点进行了分析和讨论。 相似文献
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For the first time, a genetic framework using contextual knowledge is proposed for segmentation and recognition of unconstrained handwritten numeral strings. New algorithms have been developed to locate feature points on the string image, and to generate possible segmentation hypotheses. A genetic representation scheme is utilized to show the space of all segmentation hypotheses (chromosomes). For the evaluation of segmentation hypotheses, a novel evaluation scheme is introduced, in order to improve the outlier resistance of the system. Our genetic algorithm tries to search and evolve the population of segmentation hypotheses, and to find the one with the highest segmentation/recognition confidence. The NIST NSTRING SD19 and CENPARMI databases were used to evaluate the performance of our proposed method. Our experiments showed that proper use of contextual knowledge in segmentation, evaluation and search greatly improves the overall performance of the system. On average, our system was able to obtain correct recognition rates of 95.28% and 96.42% on handwritten numeral strings using neural network and support vector classifiers, respectively. These results compare favorably with the ones reported in the literature. 相似文献
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Alexander Goltsev Author Vitae Dmitri Rachkovskij Author Vitae 《Pattern recognition》2005,38(3):315-322
The purpose of the paper is to design and test neural network structures and mechanisms for making use of the information that is contained in the character strings for more correct recognition of the characters constituting these strings. Two neural networks are considered in the paper; both networks are combined into a joint recognition system. The first is the assembly neural network and the second is the neural network of a perceptron type. A computer simulation of the system is performed. The combined system solves the task of recognition of handwritten digits of the MNIST test set provided that the digits have been arranged in the numeral strings memorized in the system. During a recognition process of an input numeral string, the assembly neural network executes intermediate recognition of the digits basing on which a perceptron type network accomplishes the final choice among the limited combinations of strings memorized in the network. The experiments have demonstrated that the combined system is able to make use of the information that is contained in the strings for more correct recognition of digits of the MNIST test set. In particular, the experiments have shown that the combined system commits no errors in the recognition of MNIST test set on the condition that the digits of this set had been organized in the strings of more than 5 digits each. 相似文献