Search Results for author:
Found 6 papers, 3 papers with code
Merge and Recognize: A Geometry and 2D Context Aware Graph Model for Named Entity Recognition from Visual Documents
By incorporating geometry information from visual documents into our model, richer 2D context information is generated to improve document representations.
LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding
The core of LayoutLLM is a layout instruction tuning strategy, which is specially designed to enhance the comprehension and utilization of document layouts.
Vision Grid Transformer for Document Layout Analysis
Document pre-trained models and grid-based models have proven to be very effective on various tasks in Document AI.
Ranked #1 on
Document Layout Analysis
on PubLayNet val
GeoLayoutLM: Geometric Pre-training for Visual Information Extraction
Additionally, novel relation heads, which are pre-trained by the geometric pre-training tasks and fine-tuned for RE, are elaborately designed to enrich and enhance the feature representation.
Ranked #1 on
Key Information Extraction
on CORD
Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understanding
Multi-modal document pre-trained models have proven to be very effective in a variety of visually-rich document understanding (VrDU) tasks.
Joint Copying and Restricted Generation for Paraphrase
In this paper, we develop a novel Seq2Seq model to fuse a copying decoder and a restricted generative decoder.
