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Florence2 EntityExtraction
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Florence2 EntityExtraction

Developed by jena-shreyas
Florence-2 DocVQA is a document visual question answering model fine-tuned based on the Microsoft Florence-2-large model, specifically designed for handling question-answering tasks in document images.
Image-to-Text
Transformers
 EnglishOpen Source License:MIT#Document Visual Question Answering#Multimodal Understanding#English Document Processing
Downloads 23
Release Time : 9/15/2024

Model Overview

This model focuses on Document Visual Question Answering (DocVQA), capable of understanding content in document images and answering related questions. Suitable for scenarios requiring information extraction from scanned or digital documents.

Model Features

Document Understanding Capability
Capable of parsing and understanding text and layout information in document images.
Question-Answering Functionality
Answers user questions regarding document content.
Based on Florence-2 Architecture
Leverages a powerful vision-language model foundation to provide high-quality document understanding capabilities.

Model Capabilities

Document Image Understanding
Visual Question Answering
Text Information Extraction

Use Cases

Document Processing
Contract Analysis
Extract key terms and condition information from legal contracts.
Quickly locate important clauses in contracts
Invoice Processing
Identify amounts, dates, and supplier information on invoices.
Automated invoice data extraction
Education
Learning Material Q&A
Answer students' questions about textbook content.
Assist the learning process
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