联机手写英文识别的解码算法

提出了一些新的方法来解决联机英文手写识别问题。基于手写英文的主笔画属性,提出了快速有效的解码算法——主笔画层次建立Beam Viterbi算法。由于整个识别过程是一个多阶段决策的过程,将多阶段的分类信息融合到决策过程中,较大幅度提高了系统的性能。在Unipen训练集和实验室数据集上测试取得了良好的效果。     

英文手写联机识别的基础模型

为了识别较为工整的英文联机手写文字,定义了适合每个字母的字元,一共有七组不同的基本子元。使用简单的二维图形学,与简单的数学计算,可以确定每个不同字母的字元性质。使用这些字元,对大小写字母与数字实施具体组合定义,因为每个字符的具体定义内容,完全各不相同,依照逻辑可以推断,能够成功迅速的识别不连笔的较为工整的英文手写字符。这种建模方法,如果移植到类似的不同国家或地区的文字,如果笔画工整,每个字符又互相独立,那么在逻辑上就可以判定很有应用价值。     

朝鲜语联机手写识别建模研究

为了解决朝鲜语(韩语)手写识别,对韩语文字提取独特几何特征定义方式.提出六组基本元定义,用它们区分韩语的各个字母.使用二次编码矫正,通过实验,使得重码率降到极低.能够较为准确的识别韩语中各种风格的手写正楷与部分行书.对于韩语草书的识别,是否成功,取决于建立大而全的草书字典.这一独特方法,也可以移植到韩语印刷体识别.     

基于多分类器集成的维吾尔文联机手写字母识别

多分类器组合能够在一定程度上弥补单个分类器的缺陷;因此它在模式识别中得到了广泛应用。深入调研国内外联机手写识别技术的研究动态;结合维吾尔文字母的独特书写风格;研究了基于多分类器集成的维吾尔语联机手写字母识别。利用5种不同的特征提取方法构造了5个独立的维吾尔语字母分类识别器;采用了等权投票和不等权投票等两种策略将5种维吾尔语字母分类识别器进行了有效组合。其中;单分类器采用了基于动态时间弯折（DTW）匹配距离的最近邻分类方法。实验结果表明;提出的集成策略的识别率明显高于单分类器的识别率;而且为特征的综合集成提供了多种有效途径。     

汉字联机手写建模方法

为了解决手机的汉字联机识别,我们提供了完整的解决方案。先定义了一套六组基本定义,形成一种特殊的但是很简单的图形描述方法。将汉字楷书的常用字的笔画,进行统计归纳归类,找到有限的笔画组成。使用基本定义,描述楷书的独立笔画,这些描述是互不相同的。回避了传统的复杂的二维图形学的方法。经过穷举法验证,几乎每个常用汉字的拆解的内容,都互不相同,所以从逻辑上可判定这种方法是有效的。也提供并公布对汉字行书、草书的部分解决方案。形成了一个手机汉字连笔识别的体系。     

联机手写化学公式识别与分析

随着移动设备的日渐普及,联机手写输入方式为化学知识的使用和分享提供了可能,而化学公式的数字化处理也逐渐成为热点。为了有效进行联机手写化学公式的识别,通过整理6种化学公式中常见的符号位置关系,提出了一种用于联机手写化学公式识别与分析的方法,该方法在处理了断笔、粘连、连笔等书写异常情况后,完成了对化学公式的切分。识别时,先利用SVM+HMM的两级分类机制识别独立的化学符号;然后以公式的语义和语法规则协助理解用户的书写原意。实验证明,该方法应用于平板电脑,对于上述3个阶段的化学公式识别均取得了理想的结果,从而为联机手写化学公式重现和重用打下了基础。     

词典驱动的联机手写日文病名识别研究

研究了一种有效的词典驱动的联机手写日文病名识别方法。病名词典以树结构存储,包含21 713个病名短语。在切分中,手写病名字符串通过分析相邻笔划之间的空间信息等特征被切分为原始的片段序列。连续的片段动态地合并为候选字符模式,不同的合并方式产生不同的候选字符序列,这样可构成一个切分候选网格。在识别过程中,结合病名词典匹配来限制候选字符模式的类别扩展,采用集束搜索策略来寻找到一条最优路径作为识别结果。用500个实际的手写病名样本做实验,平均每个病名的识别时间为0.87 s,识别正确率为83.16%。     

联机手写汉字识别技术的研究与实践

借助于模式识别的模糊数学的某些概念和思想，提出了一种获取手写汉字笔划特征的识别技术，对如何使识别系统具有自学习功能作了有益的探讨。     

自由手写拉丁字母联机识别

从实用角度出发,基于结构分析构建了自由手写体小写拉丁字母联机识别实验系统,并提出字元对象组合判别细分类算法。该算法不仅能够压缩细分类代码量数十倍,大大降低调试、扩展和维护难度,且能在一定程度上补偿训练样本收集不全造成的字典模板不足,提高系统正识率;此外,文中列举了本系统的两组分类特征。     

A survey on off-line Cursive Word Recognition

This paper presents a survey on off-line Cursive Word Recognition. The approaches to the problem are described in detail. Each step of the process leading from raw data to the final result is analyzed. This survey is divided into two parts, the first one dealing with the general aspects of Cursive Word Recognition, the second one focusing on the applications presented in the literature.     

Augmenting the Discrimination Power of HMM by NN for On-Line Cursive Script Recognition

For on-line handwriting recognition, a hybrid approach that combines the discrimination power of neural networks with the temporal structure of hidden Markov models is presented. Initially, all plausible letter components of an input pattern are detected by using a letter spotting technique based on hidden Markov models. A word hypothesis lattice is generated as a result of the letter spotting. All letter hypotheses in the lattice are evaluated by a neural network character recognizer in order to reinforce letter discrimination power. Then, as a new technique, an island-driven lattice search algorithm is performed to find the optimal path on the word hypothesis lattice which corresponds to the most probable word among the dictionary words. The results of this experiment suggest that the proposed framework works effectively in recognizing English cursive words. In a word recognition test, on average 88.5% word accuracy was obtained.     

Cursive Script Recognition using Wildcards and Multiple Experts

Variability in handwriting styles suggests that many letter recognition engines cannot correctly identify some handwritten letters of poor quality at reasonable computational cost. Methods that are capable of searching the resulting sparse graph of letter candidates are therefore required. The method presented here employs ‘wildcards’ to represent missing letter candidates. Multiple experts are used to represent different aspects of handwriting. Each expert evaluates closeness of match and indicates its confidence. Explanation experts determine the degree to which the word alternative under consideration explains extraneous letter candidates. Schemata for normalisation and combination of scores are investigated and their performance compared. Hill climbing yields near-optimal combination weights that outperform comparable methods on identical dynamic handwriting data.     

A Fuzzy Statistical Rule Generation Method for Handwriting Recognition

This paper presents a statistical approach for rule-base generation of handwriting recognition. The proposed method integrates the heuristic feature selection with the statistical evaluation and thus improves the performance of the rule generation as well as of the fuzzy handwriting recognition system. Fuzzy statistical measures are employed to identify relevant features from a given large handwriting database. First an automatic rule-base mechanism is presented. To reduce the time needed for this generation mechanism an additional heuristic feature selection step is introduced. Tests show that this generated rule-base improved the recognition results over previous approaches.     

手写体不规则数码脱机识别的研究与实现

为了进一步研究手写体不规则数码脱机识别技术,开发了手写体不规则数码识别软件,提出了一种新的识别方法——弧向判别法,对具有平滑特征的符号识别给出了一种可行的识别方法,实现中采用了查表细化法、数值重组等关键技术。实验表明,该软件识别的正确率在90%以上,大大地提高了工作效率。     

基于组合方法的人脸识别算法研究

该文试探一种加权融合流形学习的方法进行人脸识别,该算法通过Haar小波和局部线性嵌入（LLE）加权融合的方式来进行人脸识别。首先通过Haar小波变换对人脸图像进行一级分解,得到四个子图;然后利用LLE算法对四个子图降维处理,并加权融合;最后利用支持向量机（SVM）进行分类判决。通过在ORL库中进行实验,结果表明,该文提出的Haar＋LLE识别效率比单独使用主成分分析（PCA）和LLE更高效。     

Lexicon-driven HMM decoding for large vocabulary handwriting recognition with multiple character models

This paper presents a handwriting recognition system that deals with unconstrained handwriting and large vocabularies. The system is based on the segmentation-recognition paradigm where words are first loosely segmented into characters or pseudocharacters and the final segmentation is obtained during the recognition process, which is carried out with a lexicon. Characters are modeled by multiple hidden Markov models (HMMs), which are concatenated to build up word models. The lexicon is organized as a tree structure, and during the decoding words with similar prefixes share the same computation steps. To avoid an explosion of the search space due to the presence of multiple character models, a lexicon-driven level building algorithm (LDLBA) is used to decode the lexical tree and to choose at each level the more likely models. Bigram probabilities related to the variation of writing styles within the words are inserted between the levels of the LDLBA to improve the recognition accuracy. To further speed up the recognition process, some constraints are added to limit the search efforts to the more likely parts of the search space. Experimental results on a dataset of 4674 unconstrained words show that the proposed recognition system achieves recognition rates from 98% for a 10-word vocabulary to 71% for a 30,000-word vocabulary and recognition times from 9 ms to 18.4 s, respectively.Received: 8 July 2002, Accepted: 1 July 2003, Published online: 12 September 2003 Correspondence to: Alessandro L. Koerich