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Layoutlmv2 Base Uncased Finetuned Docvqa
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Layoutlmv2 Base Uncased Finetuned Docvqa

Developed by hugginglaoda
A document visual question answering model based on the LayoutLMv2 architecture, specifically fine-tuned for document understanding tasks
Image-to-Text
Transformers
#Document Visual Question Answering#Multimodal Understanding#Layout Awareness
Downloads 16
Release Time : 4/1/2023

Model Overview

This model is a fine-tuned version of LayoutLMv2 base model for Document Visual Question Answering (DocVQA) tasks, capable of understanding document layouts and content to answer document-related questions

Model Features

Multimodal Understanding Capability
Combines textual content and visual layout information for document understanding
Document Structure Awareness
Capable of recognizing and utilizing structural information such as tables and paragraphs in documents
End-to-End Question Answering
Directly extracts information from document images to answer questions without intermediate OCR steps

Model Capabilities

Document Visual Question Answering
Document Understanding
Layout Analysis
Text Localization

Use Cases

Document Processing
Form Information Extraction
Extract specific field information from scanned forms
Contract Analysis
Answer specific questions about contract terms
Education
Automatic Test Grading
Answer grading-related questions based on scanned test papers
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