基于广义置信度的样本选择算法

对模式识别系统而言,不同的训练样本在建立模式类模型时所起的作用不同,因此必须对训练样本进行选择。而在训练样本中,边界样本的判定方式以及训练样本中包含边界样本数量的多少对分类的精度起主要作用。为此,结合基于模板匹配的脱机手写汉字识别,定义了一种通过广义置信度判定边界样本的方法,并且在此基础上建立了基于广义置信度的训练样本选择算法。通过在脱机手写汉字数据库HCL2004上进行实验,由该算法选择出的训练样本集在训练样本数减少的同时,使得系统识别率有了较大的提高,从而证实了该算法的有效性。     

基于实时样本采集的个性化手写汉字输入系统设计

手写汉字识别是手写汉字输入的基础。目前智能设备中的手写汉字输入法无法根据用户的汉字书写习惯,动态调整识别模型以提升手写汉字的正确识别率。通过对最新深度学习算法及训练模型的研究,提出了一种基于用户手写汉字样本实时采集的个性化手写汉字输入系统的设计方法。该方法将采集用户的手写汉字作为增量样本,通过对服务器端训练生成的手写汉字识别模型的再次训练,使识别模型能够更好地适应该用户的书写习惯,提升手写汉字输入系统的识别率。最后,在该理论方法的基础上,结合新设计的深度残差网络,进行了手写汉字识别的对比实验。实验结果显示,通过引入实时采集样本的再次训练,手写汉字识别模型的识别率有较大幅度的提升,能够更有效的满足用户在智能设备端对手写汉字输入系统的使用需求。     

一种新的加权动态网格汉字特征抽取方法

为了更有效地提取手写汉字的特征,提高识别精度,本文提出了一种利用非线性归一化过程产生的坐标变换信息来提取手写汉字有效特征的方法。该方法通过非线性归一化获得各有效像素点在原汉字图像及规整后汉字图像中的坐标变换关系,在原图像上抽取各点特征,在归一化图像上进行网格的均匀划分和特征统计并形成用于分类的特征向量。该方法有效克服了以往先进行归一化预处理方法和动态网格方法的一些不足,兼顾了与传统结构特征提取方法的有效结合。针对HCL2000脱机手写汉字库大字符集样本的实验结果表明,该特征提取方法可有效提高识别精度和特征抽取速度。     

一种手写汉字识别结果可信度的测定方法及应用

提出一种新的手写汉字识别结果可信度的测定方法．该方法将各种识别结果的正确率作为测定识别结果可信度的绝对尺度，以各候选字的相对邻近度为基础定义了测定可信度的一种新的相对尺度，并将这两个绝对尺度和相对尺度相结合来全面测定可信度．通过基于HCL2000数据库的测试和在银行票据OCR系统中的实际应用，证明了这种方法的有效性．     

一种多模型超图用于手写汉字识别算法

随着银行业提出手填票据自动化处理需求后,对手写汉字的识别技术研究推向新的高潮。由于手写汉字形体复杂多样、训练样本不多,从而导致识别率难以提高。设计一种多模型的超图学习算法来识别手写汉字块,根据训练样本间距离关系构建样本关系阵;以样本的稀疏表示参数为样本间的关系紧密性权重构建另一个样本关系阵;以样本约束法则为基础,以标记样本间的关系权重构建标记样本间的关系阵,融合这几个关系矩阵成为多模型的超图学习框架。通过迭代学习,找出最优的手写汉字块类别归属,在手写汉字块的实验中表现出一定的优势。     

一种基于局部Gabor滤波器组的手写体汉字识别方法

针对单一尺度的Gabor滤波器组只对某一特定粗细的手写体汉字敏感的缺点，提出了一种新颖的多尺度局部Gabor滤波器组。为了评估该方法的识别性能，提出了一个基于Gabor特征的手写体汉字识别系统，实验表明多尺度全局Gabor滤波器组在识别性能上明显提高，局部Gabor滤波器组在基本保持识别性能的情况下，特征维数明显降低，计算量和内存需求减少。该方法的创新之处在于选取局部Gabor滤波器，对863 HCL2000手写体汉字数据库的识别，最高平均识别率达到了92.32%，表明了该方法在手写体汉字识别中的有效性。     

大字符集脱机手写体汉字识别粗分类问题

以HCL2000手写汉字库为基础,构建了一个实验系统,对一级汉字库3 755个汉字,使用不同的汉字特征和不同的分类距离,进行了一系列比较实验,探讨了大字符集脱机手写体汉字识别的粗分类问题,得到了一些有用的结论.     

基于分位数的手写汉字识别方法

以往的手写汉字识别方法,无论应用何种特征提取方法,在生成标准模板时,一般都采用样本特征的算术平均值。文章提出了一种使用样本特征的分位数组合生成标准模板的方法,通过其在手写汉字识别中的应用表明,该方法比基于均值的标准模板有更好的鲁棒性,且在不增加任何计算量和算法复杂度的前提下,使系统的识别性能有所提高;同时该算法还有很好的推广性能,可以应用到各种特征提取算法中。     

基于贝叶斯网络的脱机手写体汉字智能识别

针对汉字识别的超多类问题，将贝叶斯网络分类器引入小样本字符集脱机手写体汉字识别中．对手写大写数字汉字的小样本字符集构造识别系统，同时与传统的欧氏距离方法进行比较，实验表明该算法将识别率提高到92．4％，在小样本字符集脱机手写体识别中具有较强的实用性和良好的扩展性．     

基于多通道PCA模型的手写汉字识别方法

为了提高手写汉字的识别率和降低训练时间，提出了一种基于多通道PCA(Principal component analysis)模型的手写汉字识别方法．该方法首先根据汉字的结构特点，将手写汉字分解为“一”、“I”、“J”、“＼”4种方向子模式，然后分别对每个子模式进行主分量分析，最后通过建立起每类汉字的多通道PCA模型来进行手写汉字的识别．该方法既兼顾了主分量对手写汉字的描述能力，又有效地降低了建立模型的训练时间．针对1034类别的手写汉字样本的实验结果表明，该汉字识别方法的识别率较欧氏距离分类器提高了4．4个百分点，而其训练时间则明显低于直接进行PCA重建的识别方法，由此可见，该方法是有效的。     

Handwritten Chinese Character Recognition Based on Primitive and Fuzzy Features via the SEART Neural Net Model

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.     

基于主曲线算法的手写字符特征分析与提取

模式特征的提取与选择是提高手写体字符识别率的关键因素。主曲线是主成分分析的非线性推广,它是通过数据分布“中间”并满足“自相合”的光滑曲线,能够很好地描述数据分布的结构特征。利用软K段主曲线算法提取训练数据的特征,在分析手写体字符结构特点的基础上,选出手写体字符识别所使用的粗分类与细分类特征,利用这些分类特征对手写字符进行识别。该方法在CEDAR手写体数字和字符数据库上的实验表明：选取的分类特征能够有效区分相似的手写体字符,提高手写字符的识别率,为脱机手写字符识别研究提供了一种新的方法。     

A Bayesian-based method of unconstrained handwritten offline Chinese text line recognition

This paper presents a new Bayesian-based method of unconstrained handwritten offline Chinese text line recognition. In this method, a sample of a real character or non-character in realistic handwritten text lines is jointly recognized by a traditional isolated character recognizer and a character verifier, which requires just a moderate number of handwritten text lines for training. To improve its ability to distinguish between real characters and non-characters, the isolated character recognizer is negatively trained using a linear discriminant analysis (LDA)-based strategy, which employs the outputs of a traditional MQDF classifier and the LDA transform to re-compute the posterior probability of isolated character recognition. In tests with 383 text lines in HIT-MW database, the proposed method achieved the character-level recognition rates of 71.37% without any language model, and 80.15% with a bi-gram language model, respectively. These promising results have shown the effectiveness of the proposed method for unconstrained handwritten offline Chinese text line recognition.     

一种改进的脱机手写汉字四角特征粗分类方法

将粗分类应用于脱机手写汉字识别中,采用这种多层次分类策略,能有效地改善识别的性能,提高识别精度。本文提出了一种利用四角区域结构特征对手写汉字进行粗分类的方法。在对汉字基本笔画进行分析的基础之上,根据手写汉字形变的特点以及识别算法的要求,定义一组新的笔画单元,并将这些笔画单元与汉字特定区域内的结构进行比对,得到一组4位结构特征编码,以此作为脱机手写汉字粗分类的依据。对GB2312一级字库中的部分手写汉字进行采样和识别实验,结果证明改进的四角结构特征用于粗分类的有效性。     

基于深度残差网络的脱机手写汉字识别研究

手写汉字识别是模式识别与机器学习的重要研究方向和应用领域;近年来,随着深度学习理论方法的完善、新技术的层出不穷,深度神经网络在图像识别分类、图像生成等典型应用中取得了突破性的进展,其中,深度残差网络作为最新的研究成果,已成功应用于手写数字识别、图片识别分类等多个领域;将研究深度残差网络在脱机孤立手写汉字识别中的应用方法,通过改进残差学习模块的单元结构,优化深度残差网络性能,同时通过对训练集的预处理,从数据层面实现训练生成模型性能的提升,最后设计实验,验证深度残差网络、End-to-End模式在脱机手写汉字识别中的可行性,分析、总结存在的问题及今后的研究方向。     

Handwritten Chinese character recognition using fuzzy image alignment

The task of handwritten Chinese character recognition is one of the most challenging areas of human handwriting classification. The main reason for this is related to the writing system itself which encompasses thousands of characters, coupled with high levels of diversity in personal writing styles and attributes. Much of the existing work for both online and off-line handwritten Chinese character recognition has focused on methods which employ feature extraction and segmentation steps. The preprocessed data from these steps form the basis for the subsequent classification and recognition phases. This paper proposes an approach for handwritten Chinese character recognition and classification using only an image alignment technique and does not require the aforementioned steps. Rather than extracting features from the image, which often means building models from very large training data, the proposed method instead uses the mean image transformations as a basis for model building. The use of an image-only model means that no subjective tuning of the feature extraction is required. In addition by employing a fuzzy-entropy-based metric, the work also entails improved ability to model different types of uncertainty. The classifier is a simple distance-based nearest neighbour classification system based on template matching. The approach is applied to a publicly available real-world database of handwritten Chinese characters and demonstrates that it can achieve high classification accuracy and is robust in the presence of noise.     

Handwritten Chinese text editing and recognition system

This paper describes a handwritten Chinese text editing and recognition system that can edit handwritten text and recognize it with a client-server mode. First, the client end samples and redisplays the handwritten text by using digital ink technics, segments handwritten characters, edits them and saves original handwritten information into a self-defined document. The self-defined document saves coordinates of all sampled points of handwriting characters. Second, the server recognizes handwritten document based on the proposed Gabor feature extraction and affinity propagation clustering (GFAP) method, and returns the recognition results to client end. Moreover, the server can also collect the labeled handwritten characters and fine tune the recognizer automatically. Experimental results on HIT-OR3C database show that our handwriting recognition method improves the recognition performance remarkably.