59 citations · 106 across the 27 of their papers we have counts for
7 papers · 1 filter
FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning
Zhenhua Yang, Dezhi Peng, Yuxin Kong +3
Automatic font generation is an imitation task, which aims to create a font library that mimics the style of reference images while preserving the content from source images. Altho…
UPOCR: Towards Unified Pixel-Level OCR Interface
Dezhi Peng, Zhenhua Yang, Jiaxin Zhang +5
Existing optical character recognition (OCR) methods rely on task-specific designs with divergent paradigms, architectures, and training strategies, which significantly increases t…
Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation
Yongxin Shi, Dezhi Peng, Wenhui Liao +5
This paper presents a comprehensive evaluation of the Optical Character Recognition (OCR) capabilities of the recently released GPT-4V(ision), a Large Multimodal Model (LMM). We as…
ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy in Transformer
Mingxin Huang, Jiaxin Zhang, Dezhi Peng +5
In recent years, end-to-end scene text spotting approaches are evolving to the Transformer-based framework. While previous studies have shown the crucial importance of the intrinsi…
Revisiting Scene Text Recognition: A Data Perspective
Qing Jiang, Jiapeng Wang, Dezhi Peng +2
This paper aims to re-assess scene text recognition (STR) from a data-oriented perspective. We begin by revisiting the six commonly used benchmarks in STR and observe a trend of pe…
ViTEraser: Harnessing the Power of Vision Transformers for Scene Text Removal with SegMIM Pretraining
Dezhi Peng, Chongyu Liu, Yuliang Liu +1
Scene text removal (STR) aims at replacing text strokes in natural scenes with visually coherent backgrounds. Recent STR approaches rely on iterative refinements or explicit text m…
SPTS v2: Single-Point Scene Text Spotting
Yuliang Liu, Jiaxin Zhang, Dezhi Peng +8
End-to-end scene text spotting has made significant progress due to its intrinsic synergy between text detection and recognition. Previous methods commonly regard manual annotation…