Analyze scanned documents to detect and label content
Gemma-3 OCR App
中文Late Chunking Gradio服务
Process text to extract entities and details
Extract text from multilingual invoices
Parse and extract information from documents
Process and extract text from images
Find information using text queries
Fetch contextualized answers from uploaded documents
Upload and query documents for information extraction
GOT - OCR (from : UCAS, Beijing)
Extract text from images using OCR
Extract key entities from text queries
YOLOv10 Document Layout Analysis is a state-of-the-art tool designed to analyze scanned documents and identify their structural layout. It detects and labels various elements within a document, such as text, figures, tables, and headings, enabling efficient information extraction and organization. Built on the YOLOv10 architecture, this tool leverages advanced computer vision and deep learning techniques to accurately interpret and classify document content.
• Text Detection: Identifies and extracts text from scanned documents with high precision.
• Layout Understanding: Recognizes the structural organization of documents, including headers, footers, tables, and images.
• Multi-Format Support: Works with various document formats such as PDF, JPG, and PNG.
• Customizable Models: Allows users to fine-tune models for specific document types or use cases.
• Real-Time Processing: Capable of fast and accurate analysis, making it suitable for large-scale applications.
What is YOLOv10 Document Layout Analysis used for?
YOLOv10 Document Layout Analysis is primarily used for extracting and organizing information from scanned documents, such as invoices, contracts, and reports. It helps automate tasks like data entry and document classification.
What file formats does YOLOv10 Document Layout Analysis support?
The tool supports PDF, JPG, and PNG formats, making it versatile for various document types.
Do I need advanced technical skills to use YOLOv10 Document Layout Analysis?
No, the tool is designed to be user-friendly. While some technical knowledge can help with customization, basic operations can be performed by users with minimal expertise.