activity
20222024
most citedStrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training

18 citations · 31 across the 8 of their papers we have counts for

collaborators

7 papers

cs.CV2023

GridFormer: Towards Accurate Table Structure Recognition via Grid Prediction

Pengyuan Lyu, Weihong Ma, Hongyi Wang +5

All tables can be represented as grids. Based on this observation, we propose GridFormer, a novel approach for interpreting unconstrained table structures by predicting the vertex…

cs.CV20231 cited

Towards Robust Real-Time Scene Text Detection: From Semantic to Instance Representation Learning

Xugong Qin, Pengyuan Lyu, Chengquan Zhang +5

Due to the flexible representation of arbitrary-shaped scene text and simple pipeline, bottom-up segmentation-based methods begin to be mainstream in real-time scene text detection…

cs.CV20231 cited

MataDoc: Margin and Text Aware Document Dewarping for Arbitrary Boundary

Beiya Dai, Xing li, Qunyi Xie +5

Document dewarping from a distorted camera-captured image is of great value for OCR and document understanding. The document boundary plays an important role which is more evident…

cs.CV2023

ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images

Wenwen Yu, Chengquan Zhang, Haoyu Cao +24

Structured text extraction is one of the most valuable and challenging application directions in the field of Document AI. However, the scenarios of past benchmarks are limited, an…

cs.CV2023

Fast-StrucTexT: An Efficient Hourglass Transformer with Modality-guided Dynamic Token Merge for Document Understanding

Mingliang Zhai, Yulin Li, Xiameng Qin +6

Transformers achieve promising performance in document understanding because of their high effectiveness and still suffer from quadratic computational complexity dependency on the…

cs.CV202318 cited

StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training

Yuechen Yu, Yulin Li, Chengquan Zhang +7

In this paper, we present StrucTexTv2, an effective document image pre-training framework, by performing masked visual-textual prediction. It consists of two self-supervised pre-tr…