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Found 7 papers, 3 papers with code
CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large Language Models
We argue that this limitation is closely linked to the models' visual grounding capabilities.
HIP: Hierarchical Point Modeling and Pre-training for Visual Information Extraction
End-to-end visual information extraction (VIE) aims at integrating the hierarchical subtasks of VIE, including text spotting, word grouping, and entity labeling, into a unified framework.
LORE++: Logical Location Regression Network for Table Structure Recognition with Pre-training
We model TSR as a logical location regression problem and propose a new TSR framework called LORE, standing for LOgical location REgression network, which for the first time regresses logical location as well as spatial location of table cells in a unified network.
Modeling Entities as Semantic Points for Visual Information Extraction in the Wild
As the first contribution of this work, we curate and release a new dataset for VIE, in which the document images are much more challenging in that they are taken from real applications, and difficulties such as blur, partial occlusion, and printing shift are quite common.
LORE: Logical Location Regression Network for Table Structure Recognition
Table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats.
Revisiting Document Image Dewarping by Grid Regularization
This paper addresses the problem of document image dewarping, which aims at eliminating the geometric distortion in document images for document digitization.
Ranked #3 on
Local Distortion
on DocUNet
Parsing Table Structures in the Wild
In contrast to existing studies that mainly focus on parsing well-aligned tabular images with simple layouts from scanned PDF documents, we aim to establish a practical table structure parsing system for real-world scenarios where tabular input images are taken or scanned with severe deformation, bending or occlusions.
