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Non-Chinese speakers hold increasing opportunities and need to process Chinese information and communicate in Chinese. This paper, with the purpose of facilitating the handwriting input of Chinese characters for non-Chinese speakers, is directed towards the development of the handwriting rules and vocabulary for Latin-style anti-cursive characters and the ways of their selection and classification. This aims to build a practical platform by utilizing three characteristics of wavelet neural network - automatically ascertaining the number of hidden layer unit, converging rapidly and never running into the partial minimum of networks - for a simple Latin-style online handwriting input and processing, meanwhile, taking the customary handwriting habits of non-Chinese speakers. The paper, based on profound information of cursive characters, deciphered the genetic code of ancient cursive symbols and made clear the rules for characters changing into its cursive style. As a result, it breaks the bottleneck, which enables non-Chinese speakers to easily input information through handwriting Chinese characters. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of dynamic time warping (DTW) causing unintended matching correspondences when it is employed for online two-dimensional (2D) handwriting signals, and proposes the concept of dynamic positional warping (DPW) in conjunction with DTW for online handwriting matching problems. The proposed DPW allows subsignal translations without any additional costs when their starting points are matched to other points through the matching process. Because the movement of subsignals is cost-free, except for the distance between the two starting points, an adequate movement — finding and matching similar subsignals — could significantly reduce the matching cost. This feature causes a tendency to match with the least amount of subsignal movement in the result. In order to evaluate the proposed method of solving this problem, two experiments were conducted: an accuracy test for matching similar handwriting and a utility test for signature verification. For the former, we proposed the new concept of ideal matching between two handwriting signals using the affine transformation; the results of this test verified the superiority of the proposed DPW over conventional methods in reducing matching errors. Further, DPW significantly outperformed the conventional methods in the latter test. [ABSTRACT FROM AUTHOR]

This article describes a kinematic theory, called the Bêta-elliptic model, for generating handwriting movements. The model consists of a sequential controller producing a curvilinear velocity approximated by Bêta profiles. This earlier interacts with a trajectory generator to provide elliptic strokes. As an application to our model, we consider a redundant seven degrees of freedom manipulator having a kinematic structure similar to that of a human arm. We treat to demonstrate how the Bêta-elliptic theory enables a simple motor program to generate complex curvilinear movements that have many of the properties that humans exhibit when they produce cursive script. Bêta-elliptic properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. Here, we restrict our analysis to a total of seven degrees of freedom from the shoulder to the wrist. The proposed controller launches transient commands to independent hand synergies at times when the hand begins to move. The Bêta-elliptic model transforms these synergy commands into smooth curvilinear velocity fitted by Bêta profiles among temporally overlapping synergetic units of trajectory approximated by elliptic strokes. In experiments, and at first sight, good phenomenological agreement with natural movement trajectories is found. [ABSTRACT FROM AUTHOR]

In the framework of a saliency-based approach for segmenting cursive handwriting into elementary strokes, we propose a smoothing technique based on the use of a Wavelet Transform to describe the electronic ink at different resolutions. According to such an approach, derived in analogy with those proposed in the literature for early visual tasks in primates, curvature maxima corresponding to actual segmentation points are separated from those produced by the different source of noise affecting the handwriting generation process. This is obtained by evaluating the curvature maxima at different levels of resolution, and by arranging them into a pyramidal structure, from which a saliency map is eventually achieved by combining the values across all possible scales. Such maps enjoy the property of exhibiting higher values in correspondence of regions of the original ink where curvature maxima survive along difference scales, thus indicating that those regions should correspond to the parts of the ink where two strokes join. Experiments performed by using both standard databases and one of online words collected in our laboratory, have shown high performance in terms of correct segmentation, stability and predictability of the results. [ABSTRACT FROM AUTHOR]

We propose a model for the segmentation of cursive handwriting into strokes that has been derived in analogy with those proposed in the literature for early processing tasks in primate visual system. The model allows reformulating the problem of selecting on the ink the points corresponding to perceptually relevant changes of curvature as a preattentive, purely bottom-up visual task, where the conspicuity of curvature changes is measured in terms of their saliency. The modeling of the segmentation as a saliency-driven visual task has lead to a segmentation algorithm whose architecture is biologically-plausible and that does not rely on any parameter other than those that can be directly obtained from the ink. Experimental results show that the performance is very stable and predictable, thus preventing those erratic behaviors of segmentation methods often reported in the literature. They also suggest that the proposed measure of saliency has a direct relation with the dynamics of the handwriting, so as it could be used to capture in a quantitative way some aspects of cursive handwriting intuitively related to the notion of style. [ABSTRACT FROM AUTHOR]

In the framework of online cursive handwriting recognition, we present an efficient method for reordering the sequence of strokes composing handwriting in two special cases of interest: the horizontal bar of the character "t" and the dot of the character "i". The proposed method exploits shape information for selecting the strokes that most likely correspond to the features of interest, and layout and topological information for locating the strokes representing the body of the characters to which the features belong to. The method does not depend on the specific algorithm used for detecting the elementary strokes in which the electronic ink may be decomposed into. The performance of our method, evaluated on a data set of cursive words produced by 50 different writers, has shown a correct reordering of the sequence in more than 85% of the cases. Thus, the proposed method allows obtaining a more stable and invariant description of the electronic ink in terms of elementary stroke sequences, and therefore can be helpfully used as a preprocessing step for both segmentation-based and word-based handwriting recognition systems. [ABSTRACT FROM AUTHOR]