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Doclayout YOLO D4LA From Scratch

Developed by nielsr
DocLayout-YOLO is a document layout detection model based on the YOLO architecture, designed to identify and analyze various elements and structures within documents.
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Release Time : 10/29/2024

Model Overview

This model specializes in document layout analysis, capable of detecting elements such as text regions, images, and tables in documents, making it suitable for document digitization and automated processing tasks.

Model Features

Efficient Document Layout Detection
Based on the YOLO architecture, it can quickly and accurately detect various elements in documents.
Supports Multiple Document Types
Capable of handling documents with complex layouts, including text, images, and tables.
Easy Integration
Integrated via PyTorchModelHubMixin, making it easy to use and deploy on Hugging Face Hub.

Model Capabilities

Document Layout Detection
Object Detection
Text Region Recognition
Image Region Recognition
Table Detection

Use Cases

Document Digitization
Automated Document Processing
Automatically identifies and classifies different elements in documents, such as text, images, and tables.
Improves document processing efficiency and accuracy.
Information Extraction
Document Content Analysis
Extracts structured information from scanned documents.
Supports subsequent data analysis and storage.
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