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Found 8 papers, 2 papers with code
M3-VRD: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding
This paper presents a groundbreaking multimodal, multi-task, multi-teacher joint-grained knowledge distillation model for visually-rich form document understanding.
Workshop on Document Intelligence Understanding
Document understanding and information extraction include different tasks to understand a document and extract valuable information automatically.
Graph Neural Networks for Text Classification: A Survey
Text Classification is the most essential and fundamental problem in Natural Language Processing.
PDFVQA: A New Dataset for Real-World VQA on PDF Documents
Document-based Visual Question Answering examines the document understanding of document images in conditions of natural language questions.
Form-NLU: Dataset for the Form Natural Language Understanding
Compared to general document analysis tasks, form document structure understanding and retrieval are challenging.
Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis
Recognizing the layout of unstructured digital documents is crucial when parsing the documents into the structured, machine-readable format for downstream applications.
V-Doc : Visual questions answers with Documents
We propose V-Doc, a question-answering tool using document images and PDF, mainly for researchers and general non-deep learning experts looking to generate, process, and understand the document visual question answering tasks.
V-Doc: Visual Questions Answers With Documents
We propose V-Doc, a question-answering tool using document images and PDF, mainly for researchers and general non-deep learning experts looking to generate, process, and understand the document visual question answering tasks.
