activity
20182026
most citedSVTR: Scene Text Recognition with a Single Visual Model

26 citations · 28 across the 26 of their papers we have counts for

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31 papers · 1 filter

cs.CV2026

Perceptual Flow Matching for Few-Step Generative Modeling

Chuyang Zhao, Yifei Song, Hongfa Wang +7

We propose Perceptual Flow Matching (PFM), a simple yet effective framework for few-step generation in flow-matching models. Rather than performing velocity regression in the conve…

cs.CV2026

Advancing WordArt-Oriented Scene Text Recognition: Datasets and Methods

Xingsong Ye, Yongkun Du, Jiaxin Zhang +5

WordArt (artistic text) features highly customized fonts, textures, and layouts, making WordArt-oriented scene TExt Recognition (WATER) substantially more challenging than general…

cs.CV2026

ICPR 2026 Competition on Low-Resolution License Plate Recognition

Rayson Laroca, Valfride Nascimento, Donggun Kim +19

Low-Resolution License Plate Recognition (LRLPR) remains a challenging problem in real-world surveillance scenarios, where long capture distances, compression artifacts, and advers…

cs.CV2026

What Is Wrong with Synthetic Data for Scene Text Recognition? A Strong Synthetic Engine with Diverse Simulations and Self-Evolution

Xingsong Ye, Yongkun Du, JiaXin Zhang +3

Large-scale and categorical-balanced text data is essential for training effective Scene Text Recognition (STR) models, which is hard to achieve when collecting real data. Syntheti…

cs.CV2025

Complex Mathematical Expression Recognition: Benchmark, Large-Scale Dataset and Strong Baseline

Weikang Bai, Yongkun Du, Yuchen Su +2

Mathematical Expression Recognition (MER) has made significant progress in recognizing simple expressions, but the robust recognition of complex mathematical expressions with many…

cs.CV2025

UniRec-0.1B: Unified Text and Formula Recognition with 0.1B Parameters

Yongkun Du, Zhineng Chen, Yazhen Xie +6

Text and formulas constitute the core informational components of many documents. Accurately and efficiently recognizing both is crucial for developing robust and generalizable doc…