A Study on the Refining Handwritten Font by Mixing Font Styles

• URL: http://arxiv.org/abs/2505.12834v1
• Date: Mon, 19 May 2025 08:20:43 GMT
• Title: A Study on the Refining Handwritten Font by Mixing Font Styles
• Authors: Avinash Kumar, Kyeolhee Kang, Ammar ul Hassan, Jaeyoung Choi,
• Abstract summary: FontFusionGAN (FFGAN) is a novel method for improving handwritten fonts by combining them with printed fonts.<n>Our method implements generative adversarial network (GAN) to generate font that mix the desirable features of handwritten and printed fonts.
• Score: 7.965538354981952
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Handwritten fonts have a distinct expressive character, but they are often difficult to read due to unclear or inconsistent handwriting. FontFusionGAN (FFGAN) is a novel method for improving handwritten fonts by combining them with printed fonts. Our method implements generative adversarial network (GAN) to generate font that mix the desirable features of handwritten and printed fonts. By training the GAN on a dataset of handwritten and printed fonts, it can generate legible and visually appealing font images. We apply our method to a dataset of handwritten fonts and demonstrate that it significantly enhances the readability of the original fonts while preserving their unique aesthetic. Our method has the potential to improve the readability of handwritten fonts, which would be helpful for a variety of applications including document creation, letter writing, and assisting individuals with reading and writing difficulties. In addition to addressing the difficulties of font creation for languages with complex character sets, our method is applicable to other text-image-related tasks, such as font attribute control and multilingual font style transfer.

Related papers

• FontAdapter: Instant Font Adaptation in Visual Text Generation [45.00544198317519]
  We present FontAdapter, a framework that enables visual text generation in unseen fonts within seconds, conditioned on a reference glyph image.<n>Experiments demonstrate that FontAdapter enables high-quality, robust font customization across unseen fonts without additional fine-tuning during inference.
  arXiv  Detail & Related papers  (2025-06-06T08:00:49Z)
• GRIF-DM: Generation of Rich Impression Fonts using Diffusion Models [18.15911470339845]
  We introduce a diffusion-based method, termed ourmethod, to generate fonts that vividly embody specific impressions. Our experimental results, conducted on the MyFonts dataset, affirm that this method is capable of producing realistic, vibrant, and high-fidelity fonts.
  arXiv  Detail & Related papers  (2024-08-14T02:26:46Z)
• VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization [52.870638830417]
  We propose a VQGAN-based framework (i.e., VQ-Font) to enhance glyph fidelity through token prior refinement and structure-aware enhancement. Specifically, we pre-train a VQGAN to encapsulate font token prior within a codebook. Subsequently, VQ-Font refines the synthesized glyphs with the codebook to eliminate the domain gap between synthesized and real-world strokes.
  arXiv  Detail & Related papers  (2023-08-27T06:32:20Z)
• CF-Font: Content Fusion for Few-shot Font Generation [63.79915037830131]
  We propose a content fusion module (CFM) to project the content feature into a linear space defined by the content features of basis fonts. Our method also allows to optimize the style representation vector of reference images. We have evaluated our method on a dataset of 300 fonts with 6.5k characters each.
  arXiv  Detail & Related papers  (2023-03-24T14:18:40Z)
• Diff-Font: Diffusion Model for Robust One-Shot Font Generation [110.45944936952309]
  We propose a novel one-shot font generation method based on a diffusion model, named Diff-Font. The proposed model aims to generate the entire font library by giving only one sample as the reference. The well-trained Diff-Font is not only robust to font gap and font variation, but also achieved promising performance on difficult character generation.
  arXiv  Detail & Related papers  (2022-12-12T13:51:50Z)
• Font Representation Learning via Paired-glyph Matching [15.358456947574913]
  We propose a novel font representation learning scheme to embed font styles into the latent space. For the discriminative representation of a font from others, we propose a paired-glyph matching-based font representation learning model. We show our font representation learning scheme achieves better generalization performance than the existing font representation learning techniques.
  arXiv  Detail & Related papers  (2022-11-20T12:27:27Z)
• Few-Shot Font Generation by Learning Fine-Grained Local Styles [90.39288370855115]
  Few-shot font generation (FFG) aims to generate a new font with a few examples. We propose a new font generation approach by learning 1) the fine-grained local styles from references, and 2) the spatial correspondence between the content and reference glyphs.
  arXiv  Detail & Related papers  (2022-05-20T05:07:05Z)
• FontNet: Closing the gap to font designer performance in font synthesis [3.991334489146843]
  We propose a model, called FontNet, that learns to separate font styles in the embedding space where distances directly correspond to a measure of font similarity. We design the network architecture and training procedure that can be adopted for any language system and can produce high-resolution font images.
  arXiv  Detail & Related papers  (2022-05-13T08:37:10Z)
• Scalable Font Reconstruction with Dual Latent Manifolds [55.29525824849242]
  We propose a deep generative model that performs typography analysis and font reconstruction. Our approach enables us to massively scale up the number of character types we can effectively model. We evaluate on the task of font reconstruction over various datasets representing character types of many languages.
  arXiv  Detail & Related papers  (2021-09-10T20:37:43Z)
• Few-shot Compositional Font Generation with Dual Memory [16.967987801167514]
  We propose a novel font generation framework, named Dual Memory-augmented Font Generation Network (DM-Font) We employ memory components and global-context awareness in the generator to take advantage of the compositionality. In the experiments on Korean-handwriting fonts and Thai-printing fonts, we observe that our method generates a significantly better quality of samples with faithful stylization.
  arXiv  Detail & Related papers  (2020-05-21T08:13:40Z)
• Attribute2Font: Creating Fonts You Want From Attributes [32.82714291856353]
  Attribute2Font is trained to perform font style transfer between any two fonts conditioned on their attribute values. A novel unit named Attribute Attention Module is designed to make those generated glyph images better embody the prominent font attributes.
  arXiv  Detail & Related papers  (2020-05-16T04:06:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.