Feature fusion: parallel strategy vs. serial strategy

A new strategy of parallel feature fusion is introduced in this paper. A complex vector is first used to represent the parallel combined features. Then, the traditional linear projection analysis methods, including principal component analysis, K-L expansion and linear discriminant analysis, are generalized for feature extraction in the complex feature space. Finally, the developed parallel feature fusion methods are tested on CENPARMI handwritten numeral database, NUST603 handwritten Chinese character database and ORL face image database. The experimental results indicate that the classification accuracy is increased significantly under parallel feature fusion and also demonstrate that the developed parallel fusion is more effective than the classical serial feature fusion.     

From classifiers to discriminators: A nearest neighbor rule induced discriminant analysis

The current discriminant analysis method design is generally independent of classifiers, thus the connection between discriminant analysis methods and classifiers is loose. This paper provides a way to design discriminant analysis methods that are bound with classifiers. We begin with a local mean based nearest neighbor (LM-NN) classifier and use its decision rule to supervise the design of a discriminator. Therefore, the derived discriminator, called local mean based nearest neighbor discriminant analysis (LM-NNDA), matches the LM-NN classifier optimally in theory. In contrast to that LM-NNDA is a NN classifier induced discriminant analysis method, we further show that the classical Fisher linear discriminant analysis (FLDA) is a minimum distance classifier (i.e. nearest Class-mean classifier) induced discriminant analysis method. The proposed LM-NNDA method is evaluated using the CENPARMI handwritten numeral database, the NUST603 handwritten Chinese character database, the ETH80 object category database and the FERET face image database. The experimental results demonstrate the performance advantage of LM-NNDA over other feature extraction methods with respect to the LM-NN (or NN) classifier.     

核典型相关分析特征融合方法及应用

构建了一种基于核函数的典型相关分析的特征融合算法。首先,利用核函数将图像矩阵映射到核空间,再抽取同一模式的两组特征向量,在两组特征向量之间建立描述它们的相关性的判据准则函数;然后依此准则函数抽取两组典型投影矢量集;最后通过给定的特征融合策略抽取组合的典型相关特征以用于分类识别。该算法将两组特征向量之间的相关性特征作为有效鉴别信息,既可以很好地融合信息,又可以有效地去除特征之间的信息冗余,并且避免了对映射后的数据矩阵进行分解,从而简化了数据运算。在AR、PIE、ORL、Yale人脸数据库及UCI手写体数字库上的实验结果证明了该方法的有效性和稳定性。     

Handwritten numeral recognition using gradient and curvature of gray scale image

In this paper, the authors study on the use of gradient and curvature of the gray scale character image to improve the accuracy of handwritten numeral recognition. Three procedures, based on curvature coefficient, bi-quadratic interpolation and gradient vector interpolation, are proposed for calculating the curvature of the equi-gray scale curves of an input image. Then two procedures to compose a feature vector of the gradient and the curvature are described. The efficiency of the feature vectors are tested by recognition experiments for the handwritten numeral database IPTP CDROM1 and NIST SD3 and SD7. The experimental results show the usefulness of the curvature feature and recognition rate of 99.49% and 98.25%, which are one of the highest rates ever reported for these databases (H. Kato et al., Technical Report of IEICE, PRU95-3, 1995, p. 17; R.A. Wilkinson et al., Technical Report NISTIR 4912, August 1992; J. Geist et al., Technical Report NISTIR 5452, June 1994), are achieved, respectively.     

KHATT: An open Arabic offline handwritten text database

A comprehensive Arabic handwritten text database is an essential resource for Arabic handwritten text recognition research. This is especially true due to the lack of such database for Arabic handwritten text. In this paper, we report our comprehensive Arabic offline Handwritten Text database (KHATT) consisting of 1000 handwritten forms written by 1000 distinct writers from different countries. The forms were scanned at 200, 300, and 600 dpi resolutions. The database contains 2000 randomly selected paragraphs from 46 sources, 2000 minimal text paragraph covering all the shapes of Arabic characters, and optionally written paragraphs on open subjects. The 2000 random text paragraphs consist of 9327 lines. The database forms were randomly divided into 70%, 15%, and 15% sets for training, testing, and verification, respectively. This enables researchers to use the database and compare their results. A formal verification procedure is implemented to align the handwritten text with its ground truth at the form, paragraph and line levels. The verified ground truth database contains meta-data describing the written text at the page, paragraph, and line levels in text and XML formats. Tools to extract paragraphs from pages and segment paragraphs into lines are developed. In addition we are presenting our experimental results on the database using two classifiers, viz. Hidden Markov Models (HMM) and our novel syntactic classifier.     

A trainable feature extractor for handwritten digit recognition

This article focuses on the problems of feature extraction and the recognition of handwritten digits. A trainable feature extractor based on the LeNet5 convolutional neural network architecture is introduced to solve the first problem in a black box scheme without prior knowledge on the data. The classification task is performed by support vector machines to enhance the generalization ability of LeNet5. In order to increase the recognition rate, new training samples are generated by affine transformations and elastic distortions. Experiments are performed on the well-known MNIST database to validate the method and the results show that the system can outperform both SVMs and LeNet5 while providing performances comparable to the best performance on this database. Moreover, an analysis of the errors is conducted to discuss possible means of enhancement and their limitations.     

Invariant pattern recognition using contourlets and AdaBoost

In this paper, we propose new methods for palmprint classification and handwritten numeral recognition by using the contourlet features. The contourlet transform is a new two dimensional extension of the wavelet transform using multiscale and directional filter banks. It can effectively capture smooth contours that are the dominant features in palmprint images and handwritten numeral images. AdaBoost is used as a classifier in the experiments. Experimental results show that the contourlet features are very stable features for invariant palmprint classification and handwritten numeral recognition, and better classification rates are reported when compared with other existing classification methods.     

Gujarati handwritten numeral optical character reorganization through neural network

This paper deals with an optical character recognition (OCR) system for handwritten Gujarati numbers. One may find so much of work for Indian languages like Hindi, Kannada, Tamil, Bangala, Malayalam, Gurumukhi etc, but Gujarati is a language for which hardly any work is traceable especially for handwritten characters. Here in this work a neural network is proposed for Gujarati handwritten digits identification. A multi layered feed forward neural network is suggested for classification of digits. The features of Gujarati digits are abstracted by four different profiles of digits. Thinning and skew-correction are also done for preprocessing of handwritten numerals before their classification. This work has achieved approximately 82% of success rate for Gujarati handwritten digit identification.     

A novel multiset integrated canonical correlation analysis framework and its application in feature fusion

Multiset canonical correlation analysis (MCCA) is difficult to effectively express the integrated correlation among multiple feature vectors in feature fusion. Thus, this paper firstly presents a novel multiset integrated canonical correlation analysis (MICCA) framework. The MICCA establishes a discriminant correlation criterion function of multi-group variables based on generalized correlation coefficient. The criterion function can clearly depict the integrated correlation among multiple feature vectors. Then the paper presents a multiple feature fusion theory and algorithm using the MICCA method. The detailed process of the algorithm is as follows: firstly, extract multiple feature vectors from the same patterns by using different feature extraction methods; then extract multiset integrated canonical correlation features using MICCA; finally form effective discriminant feature vectors through two given feature fusion strategies for pattern classification. The multi-group feature fusion method based on MICCA not only achieves the aim of feature fusion, but also removes the redundancy between features. The experiment results on CENPARMI handwritten Arabic numerals and UCI multiple features database show that the MICCA method has better recognition rates and robustness than the fusion methods based on canonical correlation analysis (CCA) and MCCA.     

A theorem on the generalized canonical projective vectors

This paper proposes a kind of generalized canonical projective vectors (GCPV), based on the framework of canonical correlation analysis (CCA) applying image recognition. Apart from canonical projective vectors (CPV), the process of obtaining GCPV contains the class information of samples, such that the combined features extracted according to the basis of GCPV can give a better classification performance. The experimental result based on the Concordia University CENPARMI handwritten Arabian numeral database has proved that our method is superior to the method based on CPV.     

ICA和改进的SVM在有限集字符识别中的应用

介绍了独立分量分析(ICA)基本原理和算法,提出了一种基于独立分量分析和支持向量机的有限集字符识别新方法。对传统向量机解决多分类问题的“一对一”模式进行了改进,将传统向量机的“一对一”模式存在的不可分区域减小到可以忽略的程度,克服了不可分区域的影响。该算法可应用于车牌字符、手写体英文字母、手写体数字、印刷体字母、印刷体数字等有限集字符的识别。在大量的车牌汉字和手写体英文字母自动识别实验中,取得了高于95%的识别结果,证明该算法在有限集字符识别应用中的优越性。     

Handwritten numeral recognition based on simplified structural classification and fuzzy memberships

Previous handwritten numeral recognition algorithms applied structural classification to extract geometric primitives that characterize each image, and then utilized artificial intelligence methods, like neural network or fuzzy memberships, to classify the images. We propose a handwritten numeral recognition methodology based on simplified structural classification, by using a much smaller set of primitive types, and fuzzy memberships. More specifically, based on three kinds of feature points, we first extract five kinds of primitive segments for each image. A fuzzy membership function is then used to estimate the likelihood of these primitives being close to the two vertical boundaries of the image. Finally, a tree-like classifier based on the extracted feature points, primitives and fuzzy memberships is applied to classify the numerals. With our system, handwritten numerals in NIST Special Database 19 are recognized with correct rate between 87.33% and 88.72%.     

A generalised K-L expansion method which can deal with small sample size and high-dimensional problems

The K-L expansion method, which is able to extract the discriminatory information contained in class-mean vectors, is generalised, in this paper, to make it suitable for solving small sample size problems. We further investigate, theoretically, how to reduce the method’s computational complexity in high-dimensional cases. As a result, a simple and efficient GKLE algorithm is developed. We test our method on the ORL face image database and the NUST603 handwritten Chinese character database, and our experimental results demonstrate that GKLE outperforms the existing techniques of PCA, PCA plus LDA, and Direct LDA. ID="A1"Correspondance and offprint requests to: J-Y. Yang, Department of Computer Science, Nanjing University of Science and Technology, Nanjiung 210094, P.R. China. E-mail: csiyang@comp.poly.edu.hk     

识别手写汉字的基元模糊关系法

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,文章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,用汉字基元之间的模糊关系来描述汉字的结构,其优点:一是对汉字基元之间相对位置的变化有较强的适应性,二是不需要对一个汉字中的各个基元在二维平面内进行复杂地排序,汉字的结构可以简化为一个基元模糊关系的集合。     

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.     

Designing a new standard structure for improving automatic processing of Persian handwritten bank cheques

Millions of handwritten bank cheques are processed manually every day in banks and other financial institutions all over the world. Substitution of manual cheque processing with automatic cheque reader system saves time and the cost of processing. In the recent years, systems such as A2iA have been made in order to automate processing of Latin cheques. Normally, these systems are based on the standard structures of cheques such as Check 21 in the USA or Check 006 in Canada. There are major problems in traditional (currently used) Persian bank cheques, which yield low accuracy and computational cost in their automatic processing. In this paper, in order to solve these problems, a novel structure for Persian handwritten bank cheques is presented. Importance and supremacy of this new structure for Persian handwritten bank cheques is shown by conducting several experiments on our created database of cheques based on the new structure. The created database includes 500 handwritten bank cheques based on the presented structure. Experimental results verify the usefulness and importance of the new structure in automatic processing of Persian handwritten bank cheques which provides a standard guideline for automatic processing of Persian handwritten bank cheques comparable to Check 21 or Check 006.     

基于轮廓和统计特征的手写体数字识别

提出了数字规范化模板特征,并利用这一特征与轮廓分段特征相结合对手写体数字进行识别。首先使用基于轮廓分段特征的分类器进行识别,通过提高拒识率获得高可靠性的分类结果。然后由基于数字规范化模板特征的分类器对前一级分类器的拒识样本分类。实验结果表明分别基于这两个特征的分类器在分类结果上具有较强的互补性。实验的数据为真实支票上采集的10000个手写体数字样本,该方法的识别率为98.06%。     

Handwritten character recognition through two-stage foreground sub-sampling

In this paper, we present a methodology for off-line handwritten character recognition. The proposed methodology relies on a new feature extraction technique based on recursive subdivisions of the character image so that the resulting sub-images at each iteration have balanced (approximately equal) numbers of foreground pixels, as far as this is possible. Feature extraction is followed by a two-stage classification scheme based on the level of granularity of the feature extraction method. Classes with high values in the confusion matrix are merged at a certain level and for each group of merged classes, granularity features from the level that best distinguishes them are employed. Two handwritten character databases (CEDAR and CIL) as well as two handwritten digit databases (MNIST and CEDAR) were used in order to demonstrate the effectiveness of the proposed technique. The recognition result achieved, in comparison to the ones reported in the literature, is the highest for the well-known CEDAR Character Database (94.73%) and among the best for the MNIST Database (99.03%)     

联机手写维吾尔文字母识别方法

通过分析维吾尔文字母自身的结构和书写特点,提出一种联机手写维吾尔文字母识别方案,并选择在手写汉字识别技术中所提出来的归一化、特征提取及常用的分类方法,从中找出最佳的技术选择。在实验对比中,采用8种不同的归一化预处理方法,基于坐标归一化的特征提取 (NCFE) 方法,以及改进的二次分类函数(MQDF)、判别学习型二次判别函数(DLQDF)、学习矢量量化(LVQ)、支持向量机(SVM)4种分类器。同时,再考虑字符在文档中的空间几何特征,进一步提高识别性能。在128个维吾尔文字母类别、38 400个测试样本的实验中,正确识别率最高达89。08%,为进一步研究面向维吾尔文字母特性的识别技术奠定重要基础。