most citedGlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networks

12 citations · 21 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV2019

Scene Text Magnifier

Toshiki Nakamura, Anna Zhu, Seiichi Uchida

Scene text magnifier aims to magnify text in natural scene images without recognition. It could help the special groups, who have myopia or dyslexia to better understand the scene.…

cs.CV20194 cited

Serif or Sans: Visual Font Analytics on Book Covers and Online Advertisements

Yuto Shinahara, Takuro Karamatsu, Daisuke Harada +2

In this paper, we conduct a large-scale study of font statistics in book covers and online advertisements. Through the statistical study, we try to understand how graphic designers…

cs.CV20195 cited

Modality Conversion of Handwritten Patterns by Cross Variational Autoencoders

Taichi Sumi, Brian Kenji Iwana, Hideaki Hayashi +1

This research attempts to construct a network that can convert online and offline handwritten characters to each other. The proposed network consists of two Variational Auto-Encode…

cs.CV2019

A Trainable Multiplication Layer for Auto-correlation and Co-occurrence Extraction

Hideaki Hayashi, Seiichi Uchida

In this paper, we propose a trainable multiplication layer (TML) for a neural network that can be used to calculate the multiplication between the input features. Taking an image a…

cs.CV201912 cited

GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networks

Hideaki Hayashi, Kohtaro Abe, Seiichi Uchida

In this paper, we propose GlyphGAN: style-consistent font generation based on generative adversarial networks (GANs). GANs are a framework for learning a generative model using a s…

cs.CV2017

Scene Text Eraser

Toshiki Nakamura, Anna Zhu, Keiji Yanai +1

The character information in natural scene images contains various personal information, such as telephone numbers, home addresses, etc. It is a high risk of leakage the informatio…