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Detection Model of Hangul Stroke Elements: Expansion of Non-Structured Font and Influence Evaluation by Stroke Element Combinations
With the increase of various media, fonts continue to be newly developed. In Korea, numerous ‘Hangul’ fonts are also being developed, and as a result, the need for research on determining the similarity between fonts is emerging. For example, when creating a document, the font to be used must be downloaded from each computing environment. However, this is a very cumbersome process. If there is a font that is not supported in the system, the above problem can be easily solved by recommending the most similar font that can replace it. According to this need, we conducted various prior studies for similar font recommendations. As a result, we developed a ‘stroke element’ that exists in each consonant and vowel in Korean font and developed a font recommendation model using a stroke element. However, there is a limitation in that the existing research was studied only for the structured fonts corresponding to the printed type. Additionally, the font size was not considered in the font recommendation. In this study, two experiments were conducted to expand the font recommendation model by supplementing the limitations of existing studies. First, in order to enable similar font recommendations based on the stroke element even in fonts with various shapes, the font was classified according to the shape, and the stroke elements in each classification were detected. Second, when the font sizes were different, the change in the font recommendations result based on the stroke element was analyzed. In conclusion, we found that it was necessary to find a plan to extract stroke elements for font recommendation of fonts that do not belong to standard fonts. In addition, since the influence of the stroke element varies depending on the size of the font, we propose a stroke element weight model that can be used for recommendation by reflecting it.
Journal Article
FontFusionGAN: Refinement of Handwritten Fonts by Font Fusion
Handwritten fonts possess unique expressive qualities; however, their clarity often suffers because of inconsistent handwriting. This study introduces FontFusionGAN (FFGAN), a novel method that enhances handwritten fonts by mixing them with printed fonts. The proposed approach leverages a generative adversarial network (GAN) to synthesize fonts that combine the desirable features of both handwritten and printed font styles. Training a GAN on a comprehensive dataset of handwritten and printed fonts enables it to produce legible and visually appealing font samples. The methodology was applied to a dataset of handwriting fonts, showing substantial enhancements in the legibility of the original fonts, while retaining their unique aesthetic essence. Unlike the original GAN setting where a single noise vector is used to generate a sample image, we randomly selected two noise vectors, z1 and z2, from a Gaussian distribution to train the generator. Simultaneously, we input a real image into the fusion encoder for exact reconstruction. This technique ensured the learning of style mixing during training. During inference, we provided the encoder with two font images, one handwritten and the other printed font, to obtain their respective latent vectors. Subsequently, the latent vector of the handwritten font image was injected into the first five layers of the generator, whereas the latent vector of the printed font image was injected into the last two layers to obtain a refined handwritten font image. The proposed method has the potential to improve the readability of handwritten fonts, offering benefits across diverse applications, such as document composition, letter writing, and assisting individuals with reading and writing difficulties.
Journal Article
Deep Deformable Artistic Font Style Transfer
The essence of font style transfer is to move the style features of an image into a font while maintaining the font’s glyph structure. At present, generative adversarial networks based on convolutional neural networks play an important role in font style generation. However, traditional convolutional neural networks that recognize font images suffer from poor adaptability to unknown image changes, weak generalization abilities, and poor texture feature extractions. When the glyph structure is very complex, stylized font images cannot be effectively recognized. In this paper, a deep deformable style transfer network is proposed for artistic font style transfer, which can adjust the degree of font deformation according to the style and realize the multiscale artistic style transfer of text. The new model consists of a sketch module for learning glyph mapping, a glyph module for learning style features, and a transfer module for a fusion of style textures. In the glyph module, the Deform-Resblock encoder is designed to extract glyph features, in which a deformable convolution is introduced and the size of the residual module is changed to achieve a fusion of feature information at different scales, preserve the font structure better, and enhance the controllability of text deformation. Therefore, our network has greater control over text, processes image feature information better, and can produce more exquisite artistic fonts.
Journal Article
The contribution of metamemory beliefs to the font size effect on judgments of learning: Is word frequency a moderating factor?
Previous studies found that metamemory beliefs dominate the font size effect on judgments of learning (JOLs). However, few studies have investigated whether beliefs about font size contribute to the font size effect in circumstances of multiple cues. The current study aims to fill this gap. Experiment 1 adopted a 2 (font size: 70 pt vs . 9 pt) * 2 (word frequency (WF): high vs . low) within-subjects design. The results showed that beliefs about font size did not mediate the font size effect on JOLs when multiple cues (font size and WF) were simultaneously provided. Experiment 2 further explored whether WF moderates the contribution of beliefs about font size to the font size effect, in which a 2 (font size: 70 pt v s. 9 pt, as a within-subjects factor) * 2 (WF: high vs . low, as a between-subjects factor) mixed design was used. The results showed that the contribution of beliefs about font size to the font size effect was present in a pure list of low-frequency words, but absent in a pure list of high-frequency words. Lastly, a meta-analysis showed evidence supporting the proposal that the contribution of beliefs about font size to the font size effect on JOLs is moderated by WF. Even though numerous studies suggested beliefs about font size play a dominant role in the font size effect on JOLs, the current study provides new evidence suggesting that such contribution is conditional. Theoretical implications are discussed.
Journal Article
Visual Attention Adversarial Networks for Chinese Font Translation
Currently, many Chinese font translation models adopt the method of dividing character components to improve the quality of generated font images. However, character components require a large amount of manual annotation to decompose characters and determine the composition of each character as input for training. In this paper, we establish a Chinese font translation model based on generative adversarial network without decomposition. First, we improve the method of image enhancement for Chinese character images. It helps the model learning structure information of Chinese character strokes to generate font images with complete and accurate strokes. Second, we propose a visual attention adversarial network. By using visual attention block, the network catches global and local features for constructing details of characters. Experiments demonstrate our method generates high-quality Chinese character images with great style diversity including calligraphy characters.
Journal Article
Font Design—Shape Processing of Text Information Structures in the Process of Non-Invasive Data Acquisition
Computer fonts can be a solution that supports the protection of information against electromagnetic penetration; however, not every font has features that counteract this process. The distinctive features of a font’s characters define the font. This article presents two new sets of computer fonts. These fonts are fully usable in everyday work. Additionally, they make it impossible to obtain information using non-invasive methods. The names of these fonts are directly related to the shapes of their characters. Each character in these fonts is built using only vertical and horizontal lines. The differences between the fonts lie in the widths of the vertical lines. The Safe Symmetrical font is built from vertical lines with the same width. The Safe Asymmetrical font is built from vertical lines with two different line widths. However, the appropriate proportions of the widths of the lines and clearances of each character need to be met for the safe fonts. The structures of the characters of the safe fonts ensure a high level of similarity between the characters. Additionally, these fonts do not make it difficult to read text in its primary form. However, sensitive transmissions are free from distinctive features, and the recognition of each character in reconstructed images is very difficult in contrast to traditional fonts, such as the Sang Mun font and Null Pointer font, which have many distinctive features. The usefulness of the computer fonts was assessed by the character error rate (CER); an analysis of this parameter was conducted in this work. The CER obtained very high values for the safe fonts; the values for traditional fonts were much lower. This article aims to presentat of a new solution in the area of protecting information against electromagnetic penetration. This is a new approach that could replace old solutions by incorporating heavy shielding, power and signal filters, and electromagnetic gaskets. Additionally, the application of these new fonts is very easy, as a user only needs to ensure that either the Safe Asymmetrical font or the Safe Symmetrical font is installed on the computer station that processes the text data.
Journal Article
Audience Perceptions of Fonts in Projected PowerPoint Text Slides
This study examined perceptions of 10 common fonts displayed in projected PowerPoint text slides. It investigated 37 participants' ratings of the fonts-five serif and five sans serif-on four variables: comfortable-to-read, professional, interesting, and attractive.
A significant difference was found between participants' ratings of serif and sans serif fonts on the professional variable, but not on the other three variables. Significant differences in ratings were found among sans serif fonts on the professional and attractive variables and among
the serif fonts on the professional and comfortable-to-read variables. In addition, as hypothesized, a strong correlation was found between the comfortable-to-read and the professional variables. Another strong correlation was found between the interesting and the attractive variables. The
hypothesis that a negative correlation would obtain between the comfortable-to-read and the interesting variables was not borne out by the data. Technical communicators who want a font that is comfortable-to-read, professional, interesting, and attractive might choose Gill Sans, Souvenir,
or fonts that share similar anatomical characteristics.
Journal Article