Segmentation of connected handwritten numeral strings

A new approach to separating single touching handwritten digit strings is presented. The image of the connected numerals is normalized, preprocessed and then thinned before feature points are detected. Potential segmentation points are determined based on decision line that is estimated from the deepest/highest valley/hill in the image. The partitioning path is determined precisely and then the numerals are separated before restoration is applied. Experimental results on the NIST Database 19, CEDAR CD-ROM and our own collection of images show that our algorithm can get a successful recognition rate of 96%, which compares favorably with those reported in the literature.     

Segmentation and recognition system for unknown-length handwritten digit strings

The segmentation of handwritten digit strings into isolated digits remains a challenging task. The difficulty for recognizing handwritten digit strings is related to several factors such as sloping, overlapping, connecting and unknown length of the digit string. Hence, this paper aims to propose a segmentation and recognition system for unknown-length handwritten digit strings by combining several explicit segmentation methods depending on the configuration link between digits. Three segmentation methods are combined based on histogram of the vertical projection, the contour analysis and the sliding window Radon transform. A recognition and verification module based on support vector machine classifiers allows analyzing and deciding the rejection or acceptance each segmented digit image. Moreover, various submodules are included leading to enhance the robustness of the proposed system. Experimental results conducted on the benchmark dataset show that the proposed system is effective for segmenting handwritten digit strings without prior knowledge of their length comparatively to the state of the art.     

脱机手写数字识别方法

脱机手写体数字识别有着重大的使用价值,特征提取占据了重要的位置.提出了一种通过拓扑特征构造的特征提取新方法,利于了9种特征对数字进行特征提取,然后利用分类树的方法将数字进行分类.最后,在本科学生手写数字图像样本库上的试验结果表明,提出的特征提取方法不仅具有很快的运算能力,而且较大幅度地提高了识别率.     

Representation and recognition of handwritten digits usingdeformable templates

We investigate the application of deformable templates to recognition of handprinted digits. Two characters are matched by deforming the contour of one to fit the edge strengths of the other, and a dissimilarity measure is derived from the amount of deformation needed, the goodness of fit of the edges, and the interior overlap between the deformed shapes. Classification using the minimum dissimilarity results in recognition rates up to 99.25 percent on a 2,000 character subset of NIST Special Database 1. Additional experiments on an independent test data were done to demonstrate the robustness of this method. Multidimensional scaling is also applied to the 2,000×2,000 proximity matrix, using the dissimilarity measure as a distance, to embed the patterns as points in low-dimensional spaces. A nearest neighbor classifier is applied to the resulting pattern matrices. The classification accuracies obtained in the derived feature space demonstrate that there does exist a good low-dimensional representation space. Methods to reduce the computational requirements, the primary limiting factor of this method, are discussed     

提高手写体数字细化效果的改进算法

对手写的数字来说,由于墨水的扩散或字体过小,有时会造成数字图像中出现宽度远大于正常笔道宽度的疙瘩,当细化时该疙瘩会被细化为一条线或一个点,这将严重影响识别效果。为此,在细化之前,把要细化的二值数字图片的大小调整到某一个规定值,然后对其行列进行扫描,求出所有行和所有列中像素个数的最大值,分别为Max1和Max2,如果这两个最大值都大于预设的阈值,就以此点为中心,求出一个宽度为Max1的（2/5）长的内接正方形,把它所有内部点的像素值取反,就可以在细化时保形,然后对原hilditch细化算法进行一定的优化,解决了宽度为2个像素的一条直线在细化时全部被删除的情况,以及去除了在处理L型中不能去除的拐点。实验表明它的细化效果比原hilditch算法及rosenfeld算法有一定的提高。     

Segmentation of connected handwritten digits using Self-Organizing Maps

Segmentation is an important issue in document image processing systems as it can break a sequence of characters into its components. Its application over digits is common in bank checks, mail and historical document processing, among others. This paper presents an algorithm for segmentation of connected handwritten digits based on the selection of feature points, through a skeletonization process, and the clustering of the touching region via Self-Organizing Maps. The segmentation points are then found, leading to the final segmentation. The method can deal with several types of connection between the digits, having also the ability to map multiple touching. The proposed algorithm achieved encouraging results, both relating to other state-of-the-art algorithms and to possible improvements.     

Robust vision-based features and classification schemes for off-line handwritten digit recognition

We use well-established results in biological vision to construct a model for handwritten digit recognition. We show empirically that the features extracted by our model are linearly separable over a large training set (MNIST). Using only a linear discriminant system on these features, our model is relatively simple yet outperforms other models on the same data set. In particular, the best result is obtained by applying triowise linear support vector machines with soft voting on vision-based features extracted from deslanted images.     

Lexicon-driven segmentation and recognition of handwritten character strings for Japanese address reading

This paper describes a handwritten character string recognition system for Japanese mail address reading on a very large vocabulary. The address phrases are recognized as a whole because there is no extra space between words. The lexicon contains 111,349 address phrases, which are stored in a trie structure. In recognition, the text line image is matched with the lexicon entries (phrases) to obtain reliable segmentation and retrieve valid address phrases. The paper first introduces some effective techniques for text line image preprocessing and presegmentation. In presegmentation, the text line image is separated into primitive segments by connected component analysis and touching pattern splitting based on contour shape analysis. In lexicon matching, consecutive segments are dynamically combined into candidate character patterns. An accurate character classifier is embedded in lexicon matching to select characters matched with a candidate pattern from a dynamic category set. A beam search strategy is used to control the lexicon matching so as to achieve real-time recognition. In experiments on 3,589 live mail images, the proposed method achieved correct rate of 83.68 percent while the error rate is less than 1 percent.     

CUDA-enabled implementation of a neural network algorithm for handwritten digit recognition

Using a convolutional neural network as an example, we discuss specific aspects of implementing a learning algorithm of pattern recognition on the GPU graphics card using NVIDIA CUDA architecture. The training time of the neural network on a video-adapter is decreased by a factor of 5.96 and the recognition time of a test set is decreased by a factor of 8.76 when compared with the implementation of an optimized algorithm on a central processing unit (CPU). We show that the implementation of the neural network algorithms on graphics processors holds promise.     

Automatic recognition of handwritten numerical strings: a recognition and verification strategy

A modular system to recognize handwritten numerical strings is proposed. It uses a segmentation-based recognition approach and a recognition and verification strategy. The approach combines the outputs from different levels such as segmentation, recognition, and postprocessing in a probabilistic model. A new verification scheme which contains two verifiers to deal with the problems of oversegmentation and undersegmentation is presented. A new feature set is also introduced to feed the oversegmentation verifier. A postprocessor based on a deterministic automaton is used and the global decision module makes an accept/reject decision. Finally, experimental results on two databases are presented: numerical amounts on Brazilian bank checks and NIST SD19. The latter aims at validating the concept of modular system and showing the robustness of the system using a well-known database.     

The recognition of handwritten numeral strings using a two-stage HMM-based method

In this paper, a two-stage HMM-based recognition method allows us to compensate for the possible loss in terms of recognition performance caused by the necessary trade-off between segmentation and recognition in an implicit segmentation-based strategy. The first stage consists of an implicit segmentation process that takes into account some contextual information to provide multiple segmentation-recognition hypotheses for a given preprocessed string. These hypotheses are verified and re-ranked in a second stage by using an isolated digit classifier. This method enables the use of two sets of features and numeral models: one taking into account both the segmentation and recognition aspects in an implicit segmentation-based strategy, and the other considering just the recognition aspects of isolated digits. These two stages have been shown to be complementary, in the sense that the verification stage compensates for the loss in terms of recognition performance brought about by the necessary tradeoff between segmentation and recognition carried out in the first stage. The experiments on 12,802 handwritten numeral strings of different lengths have shown that the use of a two-stage recognition strategy is a promising idea. The verification stage brought about an average improvement of 9.9% on the string recognition rates. On touching digit pairs, the method achieved a recognition rate of 89.6%. Received June 28, 2002 / Revised July 03, 2002     

A genetic framework using contextual knowledge for segmentation and recognition of handwritten numeral strings

For the first time, a genetic framework using contextual knowledge is proposed for segmentation and recognition of unconstrained handwritten numeral strings. New algorithms have been developed to locate feature points on the string image, and to generate possible segmentation hypotheses. A genetic representation scheme is utilized to show the space of all segmentation hypotheses (chromosomes). For the evaluation of segmentation hypotheses, a novel evaluation scheme is introduced, in order to improve the outlier resistance of the system. Our genetic algorithm tries to search and evolve the population of segmentation hypotheses, and to find the one with the highest segmentation/recognition confidence. The NIST NSTRING SD19 and CENPARMI databases were used to evaluate the performance of our proposed method. Our experiments showed that proper use of contextual knowledge in segmentation, evaluation and search greatly improves the overall performance of the system. On average, our system was able to obtain correct recognition rates of 95.28% and 96.42% on handwritten numeral strings using neural network and support vector classifiers, respectively. These results compare favorably with the ones reported in the literature.     

基于结构聚类和笔画分析的粘连手写汉字切分

粘连手写汉字的切分是手写汉字切分中亟待解决的问题之一。因此,针对粘连手写汉字提出一种新的切分算法。该算法首先通过寻找分界线的方法来提取粘连笔段,分界线的位置是通过粘连汉字骨架图像的聚类和笔段端点类属可信度的信息来确定的。然后提取粘连笔段并对其进行分析和类型（直线或曲线）判定,从而确定切分点及切分方向。最后利用背景细化算法找到分割曲线。该算法不仅能够很好地适用于两个粘连汉字宽窄不一、含有多个粘连点等粘连情况,而且具有良好的抗噪声效果。     

Effects of classifier structures and training regimes on integrated segmentation and recognition of handwritten numeral strings

In integrated segmentation and recognition of character strings, the underlying classifier is trained to be resistant to noncharacters. We evaluate the performance of state-of-the-art pattern classifiers of this kind. First, we build a baseline numeral string recognition system with simple but effective presegmentation. The classification scores of the candidate patterns generated by presegmentation are combined to evaluate the segmentation paths and the optimal path is found using the beam search strategy. Three neural classifiers, two discriminative density models, and two support vector classifiers are evaluated. Each classifier has some variations depending on the training strategy: maximum likelihood, discriminative learning both with and without noncharacter samples. The string recognition performances are evaluated on the numeral string images of the NIST special database 19 and the zipcode images of the CEDAR CDROM-1. The results show that noncharacter training is crucial for neural classifiers and support vector classifiers, whereas, for the discriminative density models, the regularization of parameters is important. The string recognition results compare favorably to the best ones reported in the literature though we totally ignored the geometric context. The best results were obtained using a support vector classifier, but the neural classifiers and discriminative density models show better trade-off between accuracy and computational overhead.     

Farsi/Arabic handwritten digit recognition using quantum neural networks and bag of visual words method

Handwritten digit recognition has long been a challenging problem in the field of optical character recognition and of great importance in industry. This paper develops a new approach for handwritten digit recognition that uses a small number of patterns for training phase. To improve performance of isolated Farsi/Arabic handwritten digit recognition, we use Bag of Visual Words (BoVW) technique to construct images feature vectors. Each visual word is described by Scale Invariant Feature Transform (SIFT) method. For learning feature vectors, Quantum Neural Networks (QNN) classifier is used. Experimental results on a very popular Farsi/Arabic handwritten digit dataset (HODA dataset) show that proposed method can achieve the highest recognition rate compared to other state of the arts methods.     

Stabilization and SDG condition of serially connected vibrating strings system with discontinuous displacement

Based on joint feedback controls, the stabilization issue of a serially connected vibrating strings system is presented. Suppose that the two ends of the strings system are clamped and the vertical force of strings is continuous at interior nodes while the transversal displacement is discontinuous. The vertical force of strings at interior nodes are observed and the compensators are derived from the observation values. Then the feedback controllers are arranged at interior nodes to stabilize this system. Through a detailed spectral analysis, this closed loop system is proved to be asymptotically stable and there exists a sequence of (generalized) eigenvectors of the system that forms a Riesz basis with parentheses for the state space under certain conditions. Hence the spectrum‐determined‐growth (SDG) condition holds. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

混合B样条实体模型的等几何拓扑优化

等几何拓扑优化方法将经典拓扑优化理论中的有限元分析过程更改为等几何分析计算,从而提高了拓扑优化的效率与稳定性.针对现有的等几何拓扑优化方法在处理复杂实体结构优化问题时具有一定的局限性,提出一种非结构化样条实体等几何拓扑优化方法.基于混合B样条构造技术,在非结构化六面体网格上构造具有复杂结构的样条实体,并将其作为拓扑优化...