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Layoutlmv3 Cord Ner
A document understanding model fine-tuned based on LayoutLMv3-base, specifically designed for named entity recognition tasks on the CORD dataset
Downloads 26
Release Time : 5/22/2022
Model Overview
This model is specialized for named entity recognition in document images, suitable for extracting key information from structured/semi-structured documents
Model Features
Multimodal Understanding Capability
Processes both textual content and visual layout information simultaneously to improve document understanding accuracy
High-precision Entity Recognition
Achieves an F1 score of 94.8% on the CORD dataset, demonstrating excellent performance
End-to-End Training
Supports the complete workflow from raw document images to entity recognition
Model Capabilities
Document Image Analysis
Text Entity Recognition
Structured Information Extraction
Multimodal Feature Fusion
Use Cases
Document Processing
Receipt Information Extraction
Automatically identifies key information such as merchant name, date, and amount from scanned receipts
Accuracy can reach 97.6%
Table Data Extraction
Extracts structured data from complex tabular documents
Financial Automation
Invoice Processing
Automatically identifies supplier, tax ID, amount, and other information from invoices
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