activity
20192024
most citedText Recognition in the Wild: A Survey

48 citations · 86 across the 7 of their papers we have counts for

collaborators

9 papers

cs.CV2024★ 18 cited

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…

cs.CV2023★ 3 cited

Conditional Text Image Generation with Diffusion Models

Yuanzhi Zhu, Zhaohai Li, Tianwei Wang +2

Current text recognition systems, including those for handwritten scripts and scene text, have relied heavily on image synthesis and augmentation, since it is difficult to realize…

cs.CV2022★ 1 cited

SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text

Canjie Luo, Yuanzhi Zhu, Lianwen Jin +2

Large amounts of labeled data are urgently required for the training of robust text recognizers. However, collecting handwriting data of diverse styles, along with an immense lexic…

cs.CV2021

Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter

Tianwei Wang, Yuanzhi Zhu, Lianwen Jin +5

Text recognition is a popular research subject with many associated challenges. Despite the considerable progress made in recent years, the text recognition task itself is still co…

cs.CV2020★ 48 cited

Text Recognition in the Wild: A Survey

Xiaoxue Chen, Lianwen Jin, Yuanzhi Zhu +2

The history of text can be traced back over thousands of years. Rich and precise semantic information carried by text is important in a wide range of vision-based application scena…

cs.CV2020★ 8 cited

Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition

Canjie Luo, Yuanzhi Zhu, Lianwen Jin +1

Handwritten text and scene text suffer from various shapes and distorted patterns. Thus training a robust recognition model requires a large amount of data to cover diversity as mu…