共查询到20条相似文献,搜索用时 31 毫秒
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笔迹鉴定的主要过程首先是系统把手写的笔迹文字通过扫描仪输入计算机,然后对原始笔迹的图像进行预处理。在预处理阶段,本文提出了优化分割重建图像的归一化预处理方法,在参数提取阶段,本文采用多通道二维G2bro滤波器,通过计算4个方向每个方向4个频率来提取的笔迹特征。本文对10个人任意书写的笔迹进行实验,鉴别正确率得到较好的提高。 相似文献
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一种基于微结构特征的多文种文本无关笔迹鉴别方法 总被引:4,自引:0,他引:4
与字符识别一样, 计算机自动笔迹鉴别是一个涉及到不同文种的研究课题. 本文提出了一种基于网格窗口微结构特征的文本无关的笔迹鉴别方法, 能适用于各种不同文种的笔迹. 该方法对笔迹中局部细微结构的书写变化趋势进行描述, 并采用加权距离度量方法进行笔迹相似性度量. 利用该方法实现了文本无关的多文种笔迹检索系统, 并在实际汉字、英文、藏文和维吾尔文的笔迹库上进行了测试. 实验证明, 该方法是一种高效且适用性较广、限制性较少的笔迹鉴别方法. 相似文献
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Shusen Tang Zeqing Xia Zhouhui Lian Yingmin Tang Jianguo Xiao 《Computer Graphics Forum》2019,38(7):567-577
Despite the recent impressive development of deep neural networks, using deep learning based methods to generate large‐scale Chinese fonts is still a rather challenging task due to the huge number of intricate Chinese glyphs, e.g., the official standard Chinese charset GB18030‐2000 consists of 27,533 Chinese characters. Until now, most existing models for this task adopt Convolutional Neural Networks (CNNs) to generate bitmap images of Chinese characters due to CNN based models' remarkable success in various applications. However, CNN based models focus more on image‐level features while usually ignore stroke order information when writing characters. Instead, we treat Chinese characters as sequences of points (i.e., writing trajectories) and propose to handle this task via an effective Recurrent Neural Network (RNN) model with monotonic attention mechanism, which can learn from as few as hundreds of training samples and then synthesize glyphs for remaining thousands of characters in the same style. Experimental results show that our proposed FontRNN can be used for synthesizing large‐scale Chinese fonts as well as generating realistic Chinese handwritings efficiently. 相似文献
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以GB2312标准的6 763个汉字为例,首先以笔画骨架点集形式制作楷体字库标准模板;而后以同种形式采集书写者的45个手写汉字,从中提取笔画和部件等字素,计算基本书写特征并将其赋予标准字库模板,从而初步构造出该书写者的基本特征字库;接着利用机器学习的SVM和KNN方法,针对45个例字,对不同书写者例字集的结构特征进行计算,将所得结构特征赋予上述基本特征字库,最终得到书写者的个性特征字库,此即谓"优化"。最后进行的主观判断实验说明,优化后的字库有更高的接受度,本方法有望大大降低个性化字库的制作难度,降低书写者输入压力,缩短字库制作时间,节约字库制作成本。 相似文献
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Songhua Xu Hao Jiang Tao Jin Francis C.M. Lau Yunhe Pan 《Computer Graphics Forum》2008,27(7):1879-1886
To imitate personal handwritings is non‐trivial. In this paper, we attempt to address the challenging problem of automatic handwriting facsimile. We focus on Chinese calligraphic writings due to their rich variation in style, high artistic values and also the fact that they are among the most difficult candidates for the problem. We first analyze the structures and shapes of the constituent components, i.e., strokes and radicals, of characters in sample calligraphic writings by the same writer. To generate calligraphic writing in the style of the writer, we facsimile the individual character elements as well as the layout relationships used to compose the character, both in the writer's personal writing style. To test our algorithm, we compare our facsimileing results of Chinese calligraphic writings with the original writings. Our results are found to be acceptable for most cases, some of which are difficult to differentiate from the real ones. More results and supplementary materials are provided in our project website at http://www.cs.hku.hk/~songhua/facsimile/ . 相似文献
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提出一种基于滑动窗口的局部轮廓结构特征的文本无关的笔迹鉴别方法。该方法利用概率分布函数对笔迹中出现的各类局部轮廓形状结构的分布进行了描述,并采用卡方距离度量方法对笔迹进行最终的相似性度量。实验结果表明,在包含240人的HIT-MW中文笔迹库上有效地提高了鉴别正确率。 相似文献
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Analysis of stroke structures of handwritten Chinese characters 总被引:3,自引:0,他引:3
Hung-Hsin Chang Hong Yan 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》1999,29(1):47-61
Most handwritten Chinese character recognition systems suffer from the variations in geometrical features for different writing styles. The stroke structures of different styles have proved to be more consistent than geometrical features. In an on-line recognition system, the stroke structure can be obtained according to the sequences of writing via a pen-based input device such as a tablet. But in an off-line recognition system, the input characters are scanned optically and saved as raster images, so the stroke structure information is not available. In this paper, we propose a method to extract strokes from an off-line handwritten Chinese character. We have developed four new techniques: 1) a new thinning algorithm based on Euclidean distance transformation and gradient oriented tracing, 2) a new line approximation method based on curvature segmentation, 3) artifact removal strategies based on geometrical analysis, and 4) stroke segmentation rules based on splitting, merging and directional analysis. Using these techniques, we can extract and trace the strokes in an off-line handwritten Chinese character accurately and efficiently. 相似文献
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基于特征融合的脱机中文笔迹鉴别 总被引:1,自引:0,他引:1
提出一种基于文本依存笔迹特征融合的文本独立特征构造方法。建立基于方向指数直方图法笔迹特征(文本依存特征)的两因子分解模型。笔迹特征可分解成字符因子和书写因子两部分。通过两因子方差分析与数据挖掘,分离出与字符无关的书写因子,得到基于文本依存方法的文本独立特征。该方法对检材与样本笔迹的字符数量较少,特别是相同字很少或是根本没有相同字的情况下,能取得较理想的笔迹鉴别准确率,为少量字笔迹鉴别提供解决问题的思路。 相似文献
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Off-line recognition of Chinese handwriting by multifeature andmultilevel classification 总被引:1,自引:0,他引:1
Yuan Y. Tang Lo-Ting Tu Jiming Liu Seong-Whan Lee Win-Win Lin 《IEEE transactions on pattern analysis and machine intelligence》1998,20(5):556-561
In this paper, an off-line recognition system based on multifeature and multilevel classification is presented for handwritten Chinese characters. Ten classes of multifeatures, such as peripheral shape features, stroke density features, and stroke direction features, are used in this system. The multilevel classification scheme consists of a group classifier and a five-level character classifier, where two new technologies, overlap clustering and Gaussian distribution selector are developed. Experiments have been conducted to recognize 5,401 daily-used Chinese characters. The recognition rate is about 90 percent for a unique candidate, and 98 percent for multichoice with 10 candidates 相似文献
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基于纹理分析的笔迹鉴别系统 总被引:1,自引:0,他引:1
笔迹鉴别是通过分析手写字符的书写风格来判断书写人身份的一门技术。本文把手写笔迹作为一种纹理来看待,将笔迹鉴别转化为纹理识别来处理,利用多通道Gabor滤波器来提取笔迹图像的纹理特征,用支持向量机进行分类。实验中采集了17个人的不同笔迹,取得了较好的结果。 相似文献
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文章提出了一种手写汉字预分类的新方法,该方法分两步进行,首先提取笔划密度特征并用模糊规则产生四个预分类组;然后通过模糊逻辑处理将各组字符分别转换成基于非线性加权函数的模糊样板并通过基于模糊相似测量的匹配算法、相似性测量样板的分级分类进行预分类。测试结果表明,该方法效果良好,预分类正确率达到98.17%。 相似文献
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目前多数文本分类方法无法有效反映句子中不同单词的重要程度,且在神经网络训练过程中获得的词向量忽略了汉字本身的结构信息。构建一种GRU-ATT-Capsule混合模型,并结合CW2Vec模型训练中文词向量。对文本数据进行预处理,使用传统的词向量方法训练的词向量作为模型的第1种输入,通过CW2Vec模型训练得到的包含汉字笔画特征的中文词向量作为第2种输入,完成文本表示。利用门控循环单元分别提取2种不同输入的上下文特征并结合注意力机制学习文本中单词的重要性,将2种不同输入提取出的上下文特征进行融合,通过胶囊网络学习文本局部与全局之间的关系特征实现文本分类。在搜狗新闻数据集上的实验结果表明,GRU-ATT-Capsule混合模型相比TextCNN、BiGRU-ATT模型在测试集分类准确率上分别提高2.35和4.70个百分点,融合笔画特征的双通道输入混合模型相比单通道输入混合模型在测试集分类准确率上提高0.45个百分点,证明了GRU-ATT-Capsule混合模型能有效提取包括汉字结构在内的更多文本特征,提升文本分类效果。 相似文献