18 citations · 30 across the 6 of their papers we have counts for
7 papers · 1 filter
Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restoration
Yuyi Zhang, Peirong Zhang, Zhenhua Yang +5
Historical documents represent an invaluable cultural heritage, yet have undergone significant degradation over time through tears, water erosion, and oxidation. Existing Historica…
OCRGenBench: A Comprehensive Benchmark for Evaluating OCR Generative Capabilities
Peirong Zhang, Haowei Xu, Jiaxin Zhang +7
Improving visual text synthesis has long been a challenging and evolving frontier for image generation models. While recent state-of-the-art (SOTA) models have made remarkable stri…
MegaHan97K: A Large-Scale Dataset for Mega-Category Chinese Character Recognition with over 97K Categories
Yuyi Zhang, Yongxin Shi, Peirong Zhang +3
Foundational to the Chinese language and culture, Chinese characters encompass extraordinarily extensive and ever-expanding categories, with the latest Chinese GB18030-2022 standar…
Predicting the Original Appearance of Damaged Historical Documents
Zhenhua Yang, Dezhi Peng, Yongxin Shi +3
Historical documents encompass a wealth of cultural treasures but suffer from severe damages including character missing, paper damage, and ink erosion over time. However, existing…
HierCode: A Lightweight Hierarchical Codebook for Zero-shot Chinese Text Recognition
Yuyi Zhang, Yuanzhi Zhu, Dezhi Peng +5
Text recognition, especially for complex scripts like Chinese, faces unique challenges due to its intricate character structures and vast vocabulary. Traditional one-hot encoding m…
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…