Postprocessing statistical language models for handwritten Chinesecharacter recognizer

Two statistical language models have been investigated on their effectiveness in upgrading the accuracy of a Chinese character recognizer. The baseline model is one of lexical analytic nature which segments a sequence of character images according to the maximum matching of words with consideration of word binding forces. A model of bigram statistics of word-classes is then investigated and compared against the baseline model in terms of recognition rate improvement on the image recognizer. On the average, the baseline language model improves the recognition rate by about 7% while the bigram statistics model upgrades it by about 10%     

改进的MSF-VQ人脸特征提取方法

MSF-VQ是一种用于人脸识别的图像特征.它先使用预先确定的码书计算出图片的向量量化直方图特征,再通过马尔科夫稳态特征对直方图进行扩展,从而得到MSF-VQ特征.MSF-VQ特征在人脸识别中表现出较高的识别准确率.但是它在码书的确定和空间信息表达上仍有一些不足之处.针对这两个方面,本文提出了一种改进的方法.首先根据人脸数据集来计算码书,从而提高向量量化直方图对人脸的分辨能力,然后通过结合多个方向上采样的MSF特征,增加MSF-VQ特征包含的空间位置信息.实验结果表明,改进的MSF-VQ方法具有更高的人脸识别准确率.     

Recognizing chinese characters through interactive activation and competition

Most conventional approaches to Chinese character recognition attempt to recognize unknown Chinese characters per se. Hence the discriminative power of features employed play a crucial role in determining the ultimate recognition rate. However, high level linguistic knowledge such as the strong word context effects present in the Chinese language and the different frequencies of words in the daily use of the language, are not utilized in machine recognition of Chinese characters. In this paper, a word-oriented recognizer using the Interactive Activation and Competition model (IAC model) is proposed. Such a recognizer is tolerant to noise, size and font variations and it also possesses self-learning capability.     

A SVM-based cursive character recognizer

This paper presents a cursive character recognizer, a crucial module in any cursive word recognition system based on a segmentation and recognition approach. The character classification is achieved by using support vector machines(SVMs) and a neural gas. The neural gas is used to verify whether lower and upper case version of a certain letter can be joined in a single class or not. Once this is done for every letter, the character recognition is performed by SVMs. A database of 57 293 characters was used to train and test the cursive character recognizer. SVMs compare notably better, in terms of recognition rates, with popular neural classifiers, such as learning vector quantization and multi-layer-perceptron. SVM recognition rate is among the highest presented in the literature for cursive character recognition.     

说话人识别中基于聚类特征的矢量量化技术

为解决采用矢量量化的方法进行说话人识别时出现的失真问题,根据汉语语音的发音特性,提出了将矢量量化与语音特征的聚类技术相结合的方法,在进行矢量量化码书训练之前,先对特征矢量进行聚类筛选。实验结果表明,当测试语音片段长度为4 s时,在保持95％左右识别率下,采用普通矢量量化方法需64码本数,而采用该文方法只需8码本数,降低了8倍。结果说明该方法不但在一定程度上解决了因训练样本不足而引起的失真问题,而且通过方法的改进,实现了采用较低码字数产生较好的识别结果,从而提高识别效率。     

An integrated automatic face detection and recognition system

This paper proposes an integrated system for unconstrained face recognition in complex scenes. The scale and orientation tolerant system comprises a face detector followed by a recognizer. Given a color input image of a person, the face detector encloses the face from the complex scene within a circular boundary, and locates the position of the nose. A radial grid mapping centered on the nose is then performed to extract a feature vector within the boundary. The feature vector is input to a radial basis function neural network classifier for face identification. The proposed face detector achieved an average detection rate of 95.8% while the face recognizer achieved an average recognition rate of 97.5% on a database of 21 persons with variations in scale, orientation, natural illumination and background. The two modules were combined to form an automatic face recognition system that was evaluated in the context of a security system using a video database of 21 users and 10 intruders, acquired in an unconstrained environment. A recognition rate of 93.5% with 0% false acceptance rate was achieved.     

Handy: A real-time three color glove-based gesture recognizer with learning vector quantization

This paper presents Handy, a real-time hand gesture recognizer based on a three color glove. The recognizer is formed by three modules. The first module, fed by the frame acquired by a webcam, identifies the hand image in the scene. The second module, a feature extractor, represents the image by a nine-dimensional feature vector. The third module, the classifier, is performed by means of learning vector quantization. The recognizer, tested on a dataset of 907 hand gestures, has shown very high recognition rate.     

一种利用校对信息的汉字识别自适应后处理方法

后处理技术是汉字识别系统的重要组成部分。传统的识别后处理技术在很大程度上依赖于所训练的统计语言模型,没有考虑所处理文本的特殊性;而且没有利用识别器的动态识别特性。本文利用部分校对过的正确本文信息,一方面可以构建自适应语言模型,及时发现所处理文本的语言特点;另一方面可以利用识别器的动态识别特性,以修正候选字集;从而使得后续文本的识别后处理具有自适应性。40 万字的数据测试表明:这种方法的文本平均错误率较传统的后处理方法下降35.24%了,可以大大减轻数据录入人员的工作量,具有较高的实用价值。     

Building a compact online MRF recognizer for large character set by structured dictionary representation and vector quantization technique

This paper describes a method for building a compact online Markov random field (MRF) recognizer for large handwritten Japanese character set using structured dictionary representation and vector quantization (VQ) technique. The method splits character patterns into radicals, whose models by MRF are shared by different character classes such that a character model is constructed from the constituent radical models. Many distinct radicals are shared by many character classes with the result that the storage space of model dictionary can be saved. Moreover, in order to further compress the parameters, VQ technique to cluster parameter sequences of the mean vectors and covariance matrixes for MRF unary features and binary features as well as the transition probabilities of each state into groups was employed. By sharing a common parameter sequence for each group, the dictionary of the MRF recognizer can be greatly compressed without recognition accuracy loss.     

基于特征的易碎图像水印框架

提出了一种基于特征的易碎图像水印框架,来阻止VQ攻击.图像特征被提取出来,用 来处理原始水印或生成水印,使得要嵌入的水印信息不仅不被攻击者知道,而且依赖于原始图 像.因此,不同的原始图像嵌入了不同的水印信息,从而使得攻击者无法建立VQ码表,因而无法 实现VQ攻击.同时,为了提高水印的安全性和篡改的局部检测性,本文给出了该框架下水印嵌 入方法的基本要求.根据该易碎图像水印框架,本文设计了一种基于图像矩不变量的易碎水印算 法.分析和实验结果表明,该算法不仅可以很好地、局部地检测图像中的篡改.即使图像中仅有一 位被篡改时.同时,在不需任何额外的密码或图像索引号的情况下,成功地抵抗VQ攻击.     

基于预测码矢的快速搜索新算法

在ｋ维欧氏空间Ｒｋ中，给定一个有限子集Ｗ及一个向量ｘ，如何搜索Ｗ中与ｘ距离最近的向量，具有重要的实际应用价值，尤其在图象的矢量量化编码、神经网络模式识别［１］等问题中，快速搜索起决定性的作用。在分析已有快速搜索算法的基础上，给出一种新的快速搜索算法，该算法利用图象相邻块的码书地址，作为当前块的预测值，使搜索空间缩小更快。     

模糊聚类在自适应矢量量化码本训练中的应用

自适应矢量量化在语音处理中有广泛的应用,提出了一种基于ＳＦＣＭ算法的自适应矢量量化码本的训练方法,其特点是通过模糊聚类方法,重新调整训练样本与码字之间的隶属度,达到最小编码失真,使码本更适合新说话人,且计算简单,方法的实验结果表明,可以使编码平均失真下降。     

基于数学形态学的模糊集理论在车牌字符识别中的运用

为了更加高效地利用模板匹配的方法实现对车牌字符图像的识别,结合数学形态学和模糊集理论,提出基于数学形态学的模糊模板匹配方法。首先,对于二值图像的每个像素点及其8-邻域,以赋权的方式刻画中心像素点隶属于字符的程度;其次,加4×4窗口选取代表点,并有重叠地遍历整个字符图像,以构造字符图像的模糊隶属度矩阵;进而运用海明贴近度计算待识别字符的归类,实现对字符的识别;最后,使用Matlab对模糊模板匹配方法进行编程,并在实际字符图像中测试识别效果。与传统模板匹配方法相比较,测试的结果表明,车牌字符的识别准确率得到了显著的提高。     

Visual word density-based nonlinear shape normalization method for handwritten Chinese character recognition

In handwritten Chinese character recognition, the performance of a system is largely dependent on the character normalization method. In this paper, a visual word density-based nonlinear normalization method is proposed for handwritten Chinese character recognition. The underlying rationality is that the density for each image pixel should be determined by the visual word around this pixel. Visual vocabulary is used for mapping from a visual word to a density value. The mapping vocabulary is learned to maximize the ratio of the between-class variation and the within-class variation. Feature extraction is involved in the optimization stage, hence the proposed normalization method is beneficial for the following feature extraction. Furthermore, the proposed method can be applied to some other image classification problems in which scene character recognition is tried in this paper. Experimental results on one constrained handwriting database (CASIA) and one unconstrained handwriting database (CASIA-HWDB1.1) demonstrate that the proposed method outperforms the start-of-the-art methods. Experiments on scene character databases chars74k and ICDAR03-CH show that the proposed method is promising for some image classification problems.     

Unconstrained handwritten character recognition based on fuzzy logic

This paper presents an innovative approach called box method for feature extraction for the recognition of handwritten characters. In this method, the binary image of the character is partitioned into a fixed number of subimages called boxes. The features consist of vector distance (γ) from each box to a fixed point. To find γ the vector distances of all the pixels, lying in a particular box, from the fixed point are calculated and added up and normalized by the number of pixels within that box. Here, both neural networks and fuzzy logic techniques are used for recognition and recognition rates are found to be around 97 percent using neural networks and 98 percent using fuzzy logic. The methods are independent of font, size and with minor changes in preprocessing, it can be adopted for any language.     

基于可变模板和支持向量机的人体检测

随着图像处理技术和模式识别技术的发展，人体检测在监控系统、驾驶员辅助系统、图像索引等领域已得到广泛应用。针对静态图像中站姿人体检测问题，提出了一种新的特征选取方法，并应用可变模板和支持向量机相结实现对图像中的人体检测和定位。通过对图像进行轮廓提取和网格划分，选择水平方向和垂直方向上的特征组成图像的特征向量，然后使用搜集到的样本特征向量对模型进行训练，用可变模板搜索待检测图像，根据检测区域的特征和训练好的模型对图像进行分类。实验结果表明，该方法可以快速准确地检测出多种背景图像中的站姿人体,正确分类率达92%以上。     

基于Hopfield神经网络的图像矢量量化

矢量量化是图像压缩的重要方法。论文提出了基于Hopfield神经网络的图像矢量量化方法,该方法首先构造聚类表格;然后聚类表格按离散Hopfield神经网络串行方式运行;最后根据得到的最终码字集,对图像进行矢量量化。论文最后给出模拟实验和结果比较,结果表明该方法是有效的,生成的码本质量优于传统的LBG算法。     

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.     

中文口语理解弱监督训练方法

标注数据的获取一直是有监督方法需要面临的一个难题,针对中文口语理解任务中的意图识别研究了结合主动学习和自训练、协同训练两种弱监督训练方法,提出在级联框架下,从关键语义概念识别中获取语义类特征子集和句子本身的字特征子集分别作为两个"视角"的特征进行协同训练。通过在中文口语语料上进行的实验表明:结合主动学习和自训练的方法与被动学习、主动学习相比较,可以最大限度地降低人工标注量;而协同训练在很少的初始标注数据的前提下,利用两个特征子集进行协同训练,最终使得单一字特征子集上的分类错误率平均下降了0.52%。