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.
There have been attempts to analyzing handwritten characters. Bayoudh used Freeman chain-code to analyze alphabetical characters @cite_32 . Djioua and Plamondon used a Sigma lognormal model supported by the kinematic theory @cite_16 . An interactive system was developed so that the user can easily fit a Sigma-lognormal model to alphabetical characters. Wada extracted the trajectories of alphabetical characters and replaced them using a genetic algorithm @cite_20 . Zheng and Doermann adopted a thin plate spline to model an alphabetical character and generated a new alphabetical character by calculating the intermediate of the two @cite_13 . Handwriting models for robot arms were developed in @cite_3 @cite_27 .
@cite_32 @cite_3 @cite_27 @cite_16 @cite_13 @cite_20
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Recently, the Institute of Automation of Chinese Academy of Sciences (CASIA) released the unconstrained online and offline Chinese handwriting databases CASIA-OLHWDB and CASIA-HWDB, which contain isolated character samples and handwritten texts produced by 1020 writers. This paper presents our benchmarking results using state-of-the-art methods on the isolated character datasets OLHWDB1.0 and HWDB1.0 (called DB1.0 in general), OLHWDB1.1 and HWDB1.1 (called DB1.1 in general). The DB1.1 covers 3755 Chinese character classes as in the level-1 set of GB2312-80. The evaluated methods include 1D and pseudo 2D normalization methods, gradient direction feature extraction from binary images and from gray-scale images, online stroke direction feature extraction from pen-down trajectory and from pen lifts, classification using the modified quadratic discriminant function (MQDF), discriminative feature extraction (DFE), and discriminative learning quadratic discriminant function (DLQDF). Our experiments reported the highest test accuracies 89.55 and 93.22 on the HWDB1.1 (offline) and OLHWDB1.1 (online), respectively, when using the MQDF classifier trained with DB1.1. When training with both the DB1.0 and DB1.1, the test accuracies on HWDB1.1 and OLHWDB are improved to 90.71 and 93.95 , respectively. Using DFE and DLQDF, the best results on HWDB1.1 and OLHWDB1.1 are 92.08 and 94.85 , respectively. Our results are comparable to the best results of the ICDAR2011 Chinese Handwriting Recognition Competition though we used less training samples. 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. This paper presents an effective approach for the offline recognition of unconstrained handwritten Chinese texts. Under the general integrated segmentation-and-recognition framework with character oversegmentation, we investigate three important issues: candidate path evaluation, path search, and parameter estimation. For path evaluation, we combine multiple contexts (character recognition scores, geometric and linguistic contexts) from the Bayesian decision view, and convert the classifier outputs to posterior probabilities via confidence transformation. In path search, we use a refined beam search algorithm to improve the search efficiency and, meanwhile, use a candidate character augmentation strategy to improve the recognition accuracy. The combining weights of the path evaluation function are optimized by supervised learning using a Maximum Character Accuracy criterion. We evaluated the recognition performance on a Chinese handwriting database CASIA-HWDB, which contains nearly four million character samples of 7,356 classes and 5,091 pages of unconstrained handwritten texts. The experimental results show that confidence transformation and combining multiple contexts improve the text line recognition performance significantly. On a test set of 1,015 handwritten pages, the proposed approach achieved character-level accurate rate of 90.75 percent and correct rate of 91.39 percent, which are superior by far to the best results reported in the literature. Rapid movement generation models are described in the literature as an efficient tool to apprehend the handwriting behavior. Fields of application are diverse, including handwriting description, regeneration, and more recently OCR. In this paper, we propose a grapheme-based approach to offline Arabic writer identification and verification. Rather than extracting naturel graphemes from a training corpus using segmentation and clustering, it synthesizes its own graphemes based on the beta-elliptic model. Originality lies in the independence of the grapheme codebook from any training process, and the use of a model instead. One full and four partial codebooks are generated and tested. Using feature selection, raw codebooks are reduced in size with respect to FDR, FDR and cross-correlation, and random subsampling criteria. A total of 60 feature vectors are extracted using template matching, and evaluated with 411 individual writers from the IFN ENIT database. The results presented in this study demonstrated the wide representativity and the good generalization capability of synthetic codebooks. We obtained a top1 rate=90.02 and a top5 rate=96.35 for writer identification, and an EER=2.1 for writer verification. Our approach showed better properties than most of the surveyed techniques in terms of supported corpus size and identification rates. To the best of our knowledge, this study is among the first to exploit the concept of model-based synthetic codebooks in writer identification and verification. HighlightsUniversal synthetic codebooks are used for writer identification and verification.The beta-elliptic handwriting generation model is used to create the codebooks.Neither corpus-driven training nor prior segmentation is needed in this approach.Synthetic codebook demonstrated good generalization capabilities for Arabic.