This paper addresses the automatic generation of a typographic font from a subset of characters. Specifically, we use a subset of a typographic font to extrapolate additional characters. Consequently, we obtain a complete font containing a number of characters sufficient for daily use. The automated generation of Japanese fonts is in high demand because a Japanese font requires over 1,000 characters. Unfortunately, professional typographers create most fonts, resulting in significant financial and time investments for font generation. The proposed method can be a great aid for font creation because designers do not need to create the majority of the characters for a new font. The proposed method uses strokes from given samples for font generation. The strokes, from which we construct characters, are extracted by exploiting a character skeleton dataset. This study makes three main contributions: a novel method of extracting strokes from characters, which is applicable to both standard fonts and their variations; a fully automated approach for constructing characters; and a selection method for sample characters. We demonstrate our proposed method by generating 2,965 characters in 47 fonts. Objective and subjective evaluations verify that the generated characters are similar to handmade characters.
This paper is basically concerned with a practical problem: the on-the-fly quick learning of handwritten character recognition systems. More generally, it explores the problem of generating new learning examples, especially from very scarce (2 to 5 per class) original learning data. It presents two different methods. The first one is based on applying distortions on original characters using knowledgeon handwriting properties like speed, curvature etc. The second one consists in generation based on the notion of analogical dissimilaritywhich quantifies the analogical relation "Ais to Balmost as Cis to D". We give an algorithm to compute the k-least dissimilar objects D, hence generating knew objects from three examples A, Band C. Finally, we experimentally prove the efficiency of both methods, especially when used in conjunction.   The Sigma-Lognormal model of the kinematic theory of rapid human movements, has been implemented in an interactive software tool, allowing the generation of databases of unlimited size from a few online handwriting specimens of letters and words. Online trajectories of a target word produced by a few writers are fitted by the Sigma-Lognormal parameters; using the interactive system. Thereafter, the fiducial pattern of the word is constructed and the writer variability is circumscribed respectively from the mean values and the standard deviations of the extracted parameters. Typical simulation results obtained by randomly fixing the parameters inside these realistic intervals are presented to highlight the ability of the generator to produce a large variety of multi-writer and writer-dependent handwriting patterns as observed in real data. Overall, this software tool provides new insights on the development of huge databases for the training and testing of online handwriting classifiers and recognizers. Since it is extremely expensive to collect a large volume of handwriting samples, synthesized data are often used to enlarge the training set. We argue that, in order to generate good handwriting samples, a synthesis algorithm should learn the shape deformation characteristics of handwriting from real samples. In this paper, we present a point matching algorithm to learn the deformation, and apply it to handwriting synthesis. Preliminary experiments show the advantages of our approach. In pattern recognition, a large number of diversiform characters is necessary to train test a handwritten character recognition system. In this paper, we show that a handwriting model can be applied to the diversification of characters. The characters diversified by the model can be used as a database of character images for training testing purposes. Wada-Kawato's handwriting model is based on an optimal principle and the feature space of the characters includes sets of via-points extracted from actual handwritten characters. The handwriting model can be used to generate a variety of characters by changing via-point information. In this paper, we propose a method for generating a large variety of characters by changing via-point information based on a genetic algorithm, and show that the accuracy of a handwritten character recognition system that uses the characters generated by the proposed method as the training data, is equivalent to that of a system composed by using natural data.