9 citations · 16 across the 7 of their papers we have counts for
7 papers
Brush Your Text: Synthesize Any Scene Text on Images via Diffusion Model
Lingjun Zhang, Xinyuan Chen, Yaohui Wang +2
Recently, diffusion-based image generation methods are credited for their remarkable text-to-image generation capabilities, while still facing challenges in accurately generating m…
Weakly Supervised Scene Text Generation for Low-resource Languages
Yangchen Xie, Xinyuan Chen, Hongjian Zhan +4
A large number of annotated training images is crucial for training successful scene text recognition models. However, collecting sufficient datasets can be a labor-intensive and c…
VGTS: Visually Guided Text Spotting for Novel Categories in Historical Manuscripts
Wenbo Hu, Hongjian Zhan, Xinchen Ma +3
In the field of historical manuscript research, scholars frequently encounter novel symbols in ancient texts, investing considerable effort in their identification and documentatio…
DGFont++: Robust Deformable Generative Networks for Unsupervised Font Generation
Xinyuan Chen, Yangchen Xie, Li Sun +1
Automatic font generation without human experts is a practical and significant problem, especially for some languages that consist of a large number of characters. Existing methods…
SGBANet: Semantic GAN and Balanced Attention Network for Arbitrarily Oriented Scene Text Recognition
Dajian Zhong, Shujing Lyu, Palaiahnakote Shivakumara +4
Scene text recognition is a challenging task due to the complex backgrounds and diverse variations of text instances. In this paper, we propose a novel Semantic GAN and Balanced At…
DG-Font: Deformable Generative Networks for Unsupervised Font Generation
Yangchen Xie, Xinyuan Chen, Li Sun +1
Font generation is a challenging problem especially for some writing systems that consist of a large number of characters and has attracted a lot of attention in recent years. Howe…
Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution
Yue Lu, Yun Zhou, Zhuqing Jiang +2
Convolutional neural networks (CNNs) have demonstrated superior performance in super-resolution (SR). However, most CNN-based SR methods neglect the different importance among feat…