1)off-line handwritten Chinese character recognition脱机手写体汉字识别
1.Algorithms Research on Thinning, Feature Extracting and Similar Chinese Characters Recognition for Off-line Handwritten Chinese Character Recognition;脱机手写体汉字识别中细化、特征提取和相似字识别算法研究
2.The proposed off-line handwritten Chinese character recognition system was composed of a feature extraction module and a recognition module.提出的脱机手写体汉字识别系统主要研究特征提取和分类识别两个模块。
3.This paper researches on feature extraction methods for off-line handwritten Chinese character recognition applied to automatic recognition of financial bills.针对金融票据自动识别应用中的脱机手写体汉字识别进行特征提取的研究,首先提出了用Gabor特征和Zernike矩特征来分别表征汉字的局部特征和全局特征。
英文短句/例句
1.The Research of Large-Set Offline Handwritten Chinese Characters Recognition;大字符集脱机手写体汉字识别的研究
2.Off-line Handwritten Chinese Character Recognition Based on Bandelet;基于bandelet的脱机手写体汉字识别研究
3.Research on Off-line Handwritten Chinese Character Recognition Based on SVM基于SVM的脱机手写体汉字识别研究
4.Offline recognition of hand-written Chinese characters based on a multi-Agent system基于多Agent系统的脱机手写体汉字识别
5.Integration of Fine Recognition in Off-line Handwritten Chinese Character Recognition基于细识别的脱机手写体汉字识别的集成
6.The Research of Off-Line Hand-Written Chinese Character Recognition Based on Sub-Stroke;基于笔段的脱机手写体汉字识别方法研究
7.Research and Implementation of Feature Extraction Based on Offline Handwritten Chinese Recognition;脱机手写体汉字识别的特征提取研究与实现
8.The Research on Neural Network for Off-line Handwritten Chinese Characters Recognition;用于脱机手写体汉字识别的神经网络的研究
9.Research on Off-line Handwritten Chinese Characters Recognition Based on SVM基于SVM的脱机手写体汉字识别方法的研究
10.A off-Line Handwritten Chinese Character Recognition Based on the Binary Tree of SVM Multi-Classification基于二叉树多层分类SVM的脱机手写体汉字识别
11.Algorithms Research on Thinning, Feature Extracting and Similar Chinese Characters Recognition for Off-line Handwritten Chinese Character Recognition;脱机手写体汉字识别中细化、特征提取和相似字识别算法研究
12.Research on Method of Off-line Handwritten Chinese Characters Recognizing by Supports Vector Machine;基于支持向量机的脱机手写体汉字识别方法的研究
13.The Research of Multi-Classifier Ensemble Based on Offline Handwritten Chinese Recognition;多分类器集成的脱机手写体汉字识别方法的研究
14.Recognition of Off-line Handwritten Chinese Character by Using Decision Tree Based on Hiberarchy Decomposition基于层次分解决策树的脱机手写体汉字识别研究
15.Research on Handwritten Chinese Character Recognition Method Based on Rough Set and Genetic Algorithm基于粗糙集和遗传算法的脱机手写体汉字识别方法研究
16.Research of Recognition on Off-line Handwritten Chinese Characters Based on Procedure Neural Networks基于过程神经元网络的脱机手写体汉字识别方法研究
17.A Research of Biomimetic Recognition on Off-line Handwritten Chinese Characters;一种脱机手写体汉字仿生识别方法的研究
18.Research of Handwritten Chinese Characters Multi-Classification Recognition Based on Structure Features of Characters;基于字型特征的脱机手写体汉字多分类识别的研究
相关短句/例句
Off-line handwritten Chinese character recognition脱机手写汉字识别
3)online handwritten Chinese character recognition联机手写体汉字识别
1.In order to cope with stroke order variations , stroke number variations and large shape variations, a new online handwritten Chinese character recognition method is presented.为了解决联机手写体汉字笔划顺序、笔划数目及笔划形状变化问题,提出了一种新的联机手写体汉字识别方法:人工神经网络(ANN)和隐马尔可夫模型(HMM)相结合的汉字识别方法,首先通过BP神经网络进行笔划识别,再通过笔划类型和笔划间位置关系的隐马尔可夫模型进行整字识别。
4)handwritten Chinese character recognition手写体汉字识别
1.Small Set Handwritten Chinese Character Recognition Based on Integration RBF Neural Network;基于集成RBF神经网络的小类别手写体汉字识别系统
2.By choosing stroke and substroke as basic components to analyse the composition regularities and deformation regularities of handwritten Chinese character, two models are presented for representing the structure for handwritten Chinese character recognition: the substroke center model and the stroke relation matrix model, as the classifying criteria and methods based on models.针对手写体汉字识别问题,选取笔段和笔划作为基元,分析手写体汉字的组成规律和变形规律,提出了两种汉字结构模型:笔段中心点模型和笔划关系矩阵模型,以及基于模型的分类依据和识别方法。
3.According to the feature extraction of the handwritten Chinese character recognition, an improved extracting stroke plane method was proposed.针对手写体汉字识别过程中的特征抽取,提出了一种改进的抽取笔画平面的方法。
5)handwritten Chinese characters recognition手写体汉字识别
1.A system of handwritten Chinese characters recognition based on feedback structure is construc- ted.构建一种基于反馈结构的手写体汉字识别系统。
6)HCCR汉字手写体识别
延伸阅读
汉字识别基本方法汉字识别基本方法basic method of Chinese character recognition hanzi shibie liben fQngfQ汉字识别基本方法(basic method of Chin.记chal，ctern沈摊贝lt10n)应用模式识别原理对输人计算机的汉字进行自动识别的基本处理方法。可归结为统计决策方法和句法结构方法两类。 统计决策方法是提取待识别输人模式(例如一个汉字)的一组统计特征，形成该模式的多维特征向量，再依据一定准则确定的决策函数，和已存储在机器中的一批标准识别模式(例如3755个汉字)的多维特征向量集合匹配判别，找出其特征最接近输人模式的标准模式，该标准模式就作为未知的输人模式。例如，在3 755个汉字范围内识别汉字。设每个汉字的特征向量有m维，求出3755个m维标准汉字的特征向量口，(每个标准汉字的特征向量是一批字形互有差别的特征向量的平均值)储存在机器中。马=二1，2，…，3 755(1)厂卜|l曰||||||||L要识别一个输人汉字，就是找出与输人汉字的m维特征向量X最接近的标准汉字特征向量集里的一个特征向量口，(lell，2，…，3755})，它所代表的汉字就是识别结果。实际上，汉字识别总是先分类(参见汉字分类识别)，再在分到的每类中判别出待识汉字。在分类时，岛是3755个汉字分为k类(k镇3755)的标准特征向量中的某一类(l二1，2，…，k)，X是包含待识汉字在内的从类中的特征向量。类似于式(1)的判别结果是分类，即找到包含输人待识汉字的那一类。 用统计决策方法分类判别时，主要用的判别准则是距离D和类似度R。距离D常用的是绝对值距离D(X，G)。D(x，。)=艺}二一。(2)式中X=(x1，xZ，…，x。)—输人未知汉字的特 征向量， G=(91，g2，…，乳)一‘一储存在机器中的一 个标准汉字特征向量， m—特征向量维数。 两个向量X、G的类似度R(X，G)定义为R(X，G)二 习x，·二!争:·郭:}‘(3)当两个向量完全相同时，D(x，G)=0，R(x，G)=1。若把绝对值距离规范化，并记为DO，有D。(X，G，一素)客，/，一}}(‘，对该向量的每一个分量，假设输人和标准一致时，差值为0，式(3)中积为1，平方值为1;否则差值为1，式(3)中积为0，平方值为1，则有 R(X，G)=l一Do(X，G)(5)可见距离和类似度之间存在着相关性。当输人汉字和储存在机器中的标准汉字之间的D最小或R最大时，即被识别。 句法绪构方法汉字是一种特殊的二维图形，其结构信息既丰富又有规律性。
