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手写汉字中笔划,部件及其位置关系均产生较大变化,这种变化是引起手写汉字特征不稳定的主要因素。为了减小上述不利影响,使手写汉字特征的描述趋于稳定,本文给出了一种基于汉字基元之间的模糊关系识别手写汉字的方法。 相似文献
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本文通过分析传统汉字的结构模型所具有的优缺点,提出了建立脱机手写汉字统计模型的理论框架;并利用PCA技术发现大量数据规律性的能力,提出了一种基于PCA技术的脱机手写汉字的统计模型.与传统的结构模型相比,该模型避免了目前还无法解决的准确抽取结构基元的困难,通过以容易抽取的可重构的统计特征作为统计基元,并通过对统计基元变化的整体描述或者说对统计基元相互之间关系的描述,较好地建立了脱机手写汉字的统计模型.根据该模型得到的一些实验结果充分说明了其描述脱机手写汉字的有效性. 相似文献
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手写印刷体汉字相关属性关系图启发式匹配法 总被引:4,自引:0,他引:4
在手写印刷体汉字识别的研究中,汉字的总体结构特征渐渐体现出了它的重要性,人在识字时,也只是掌握了汉字结构的一种抽象描述,只要汉字的结构偏差在一定范围内,人就可以进行非精确匹配,将该字识别出来,我们详细分析了手写印刷体汉字的结构特征,认为手写印刷体汉字最稳定的结构特征是汉字笔划段之间的相对位置关系。因此如 相似文献
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脱机手写汉字机器识别方法的研究是人机接口自然化和智能化进程中的一个重要课题。目前,对于印刷体汉字的识别已取得了满意效果,出现了“读”书的机器,但对于脱机手写汉字的识别,还需要进一步探索和研究。本文就将探讨针对这一难题的各种研究方法。◆ 结构模式识别方法结构模式识别是早期脱机手写汉字识别研究的主要方法。集中在如何准确地抽取基元、轮廓、特征点等能够反映汉字结构信息的特征上。通常,抽取笔画需要进行细化处理,但是细化算法不仅速度慢,且易产生伪笔画段,单纯采用结构模式识别方法已不能满足脱机手写汉字识别系统… 相似文献
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小类别数手写汉字建模 总被引:4,自引:1,他引:3
在手写汉字识别的研究中,鲜有研究者提出建立手写汉字的数学模型,本文在这方面作了一些探讨。建模的目的通常有两个:一是手写汉字的表示或描述,二是手写汉字的识别。本文针对小类别数手写汉字,在骨架图形的基础上,把手写汉字看作孤枝、孤环和部件的集合,并定义三者之间的方位关系,从而建立手写汉字的数学模型。实验表明,该模型用于识别,效果良好。 相似文献
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一种手写汉字拓扑图表示及其动态获取 总被引:2,自引:0,他引:2
手写汉字的机器识别,属于图像模式分类问题。所谓图像模式分类,指的是把一定范围内的图像,分成预先确定的类别,然后再去对给定范围内的图像进行识别分类。显然,这其中预先确定类属性特征、类标准模板以及具体分类识别策略都是图像模式识别的关键问题。对于我们具体的手写汉字识别问题,由于单字结构分析和基元形态分析一起可以给出汉字形体的完整描述,而统计形态分析却难以满足大规模汉字字集的集群性和分离性要求。 相似文献
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为将统计决策方法和句法方法有机结合起来, 本文提出了以部件为基元的基于假设检验的手写印, 体汉字识别方法由统计方法得到候补字集, 利用部件特征的先验知识抽取待识字可能包含的部件并对假设进行验证, 从而不断缩小候补字集, 并逐步完善汉字的结构描述。初步实验表明其分类效果明显。 相似文献
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本文为手写印刷体汉字识别提供了一种新的解决方法。在研究过程中, 从汉字图象的输入到识别结果的获取, 建立了一整套基本完整的识别实验系统。系统选择四边形状特征作为粗分类的基本特征, 提出汉字最稳定的结构是笔划段之间相对位置关系的思想。在粗分类时引入集合运算, 提高了粗分类的正确率和分类能力, 在细分时用快速合并笔划段的方法获取汉字笔划段作为细分特征。最后对于关系结构图的匹配提出了一种新的匹配方法一相关属性关系图启发式匹配,这种方法利用了汉字样本知识, 建立具有相关属性的关系图, 在其指导下, 完成非精确的结构匹配, 该系统在386微机上用汇编语言实现, 对1千个手写常用汉字识别率达百90%以上, 速度是每字2秒。 相似文献
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Chinese characters are constructed by strokes according to structural rules. Therefore, the geometric configurations of characters are important features for character recognition. In handwritten characters, stroke shapes and their spatial relations may vary to some extent. The attribute value of a structural identification is then a fuzzy quantity rather than a binary quantity. Recognizing these facts, we propose a fuzzy attribute representation (FAR) to describe the structural features of handwritten Chinese characters for an on-line Chinese character recognition (OLCCR) system. With a FAR. a fuzzy attribute graph for each handwritten character is created, and the character recognition process is thus transformed into a simple graph matching problem. This character representation and our proposed recognition method allow us to relax the constraints on stroke order and stroke connection. The graph model provides a generalized character representation that can easily incorporate newly added characters into an OLCCR system with an automatic learning capability. The fuzzy representation can describe the degree of structural deformation in handwritten characters. The character matching algorithm is designed to tolerate structural deformations to some extent. Therefore, even input characters with deformations can be recognized correctly once the reference dictionary of the recognition system has been trained using a few representative learning samples. Experimental results are provided to show the effectiveness of the proposed method. 相似文献
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A handwritten Chinese character recognition method based on primitive and compound fuzzy features using the SEART neural network model is proposed. The primitive features are extracted in local and global view. Since handwritten Chinese characters vary a great deal, the fuzzy concept is used to extract the compound features in structural view. We combine the two categories of features and use a fast classifier, called the Supervised Extended ART (SEART) neural network model, to recognize handwritten Chinese characters. The SEART classifier has excellent performance, is fast, and has good generalization and exception handling abilities in complex problems. Using the fuzzy set theory in feature extraction and the neural network model as a classifier is helpful for reducing distortions, noise and variations. In spite of the poor thinning, a 90.24% recognition rate on average for the 605 test character categories was obtained. The database used is CCL/HCCR3 (provided by CCL, ITRI, Taiwan). The experiment not only confirms the feasibility of the proposed system, but also suggests that applying the fuzzy set theory and neural networks to recognition of handwritten Chinese characters is an efficient and promising approach. 相似文献
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手写汉字评价推动了计算机辅助教学的快速发展,如何通过手写汉字评价技术,在无教师帮助的情况下实现手写汉字的等级/规范性评价是当前研究的重点。对手写汉字评价相关概念以及发展趋势进行阐述;对手写汉字评价的不同研究方法进行详细介绍,包括基于规则、特征相似度计算、模糊矩阵以及机器学习等方面,并对各种方法的优缺点进行总结归纳;对手写汉字评价的反馈形式进行介绍,包括数据到文本生成、字形匹配与图形辅助等方面;分析手写汉字评价面临的多个问题,进一步思考其未来的发展。 相似文献
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Shuyan ZhaoAuthor Vitae Zheru ChiAuthor Vitae Penfei ShiAuthor VitaeHong YanAuthor Vitae 《Pattern recognition》2003,36(1):145-156
Correct segmentation of handwritten Chinese characters is crucial to their successful recognition. However, due to many difficulties involved, little work has been reported in this area. In this paper, a two-stage approach is presented to segment unconstrained handwritten Chinese characters. A handwritten Chinese character string is first coarsely segmented according to the background skeleton and vertical projection after a proper image preprocessing. With several geometric features, all possible segmentation paths are evaluated by using the fuzzy decision rules learned from examples. As a result, unsuitable segmentation paths are discarded. In the fine segmentation stage that follows, the strokes that may contain segmentation points are first identified. The feature points are then extracted from candidate strokes and taken as segmentation point candidates through each of which a segmentation path may be formed. The geometric features similar to the coarse segmentation stage are used and corresponding fuzzy decision rules are generated to evaluate fine segmentation paths. Experimental results on 1000 Chinese character strings from postal mail show that our approach can achieve a reasonable good overall accuracy in segmenting unconstrained handwritten Chinese characters. 相似文献
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基于可伸缩矢量图SVG的在线手写汉字是以SVG图像作为汉字图像格式、以SVG的path对象作为笔画的基本存储单元来对汉字进行显示和存储的,笔画的轮廓是以手写过程中记录的坐标值作为特征数值加以确定的。基于此种SVG手写汉字存储和表示形式,本文提出一种基于图论的在线连续手写汉字多步分割方法。该方法根据汉字笔画间的坐标位置关系对手写笔画序列构建无向图模型,并利用图的广度优先搜索将原笔画序列分割为互不连通的笔画部件,使偏旁部首分离较远、非粘连汉字得到正确分割;然后利用改进的tarjan算法对部件中的粘连字符进行分割,最后基于笔画部件间距,利用二分类迭代算法对间距进行分类,找出全局最佳分割位置,对过分割的部件进行重组合并。实验结果表明,该方法对于在线手写汉字的分割是有效可行的。 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