Layoutlmv3 Finetuned Sroie
A document understanding model fine-tuned on the SROIE dataset based on Microsoft's LayoutLMv3-base model, excelling in extracting structured information from scanned documents
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Release Time : 6/7/2022
Model Overview
This model is specifically designed for document information extraction tasks, capable of identifying and classifying key fields in documents such as receipts (e.g., date, amount, merchant information)
Model Features
High-precision Document Understanding
Achieves 94% F1 score on the SROIE dataset, accurately identifying key information in receipts
Multimodal Processing Capability
Simultaneously processes text content and visual layout information to enhance document understanding
End-to-end Training
Supports direct information extraction from raw document images without complex preprocessing
Model Capabilities
Receipt Information Extraction
Document Entity Recognition
Structured Data Generation
Visual Text Understanding
Use Cases
Financial Automation
Receipt Information Digitization
Automatically extracts merchant, amount, date, and other information from scanned receipts
Accuracy exceeds 99%, with an F1 score of 94%
Document Processing
Invoice Information Extraction
Identifies key fields in invoices and generates structured data
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