Document parsing core functionality
- OCR recognition: Accurately extract text content from documents
- form recognition: Automatically parses table structure and extracts data
- DocVQA (Documentation Question and Answer): Get document-specific information through natural language questioning
- Documentation Summary: Automatically generate summaries of document content
Handling complex document processes
- Upload a document: Support for scanned documents, PDF, images and other formats
- preprocessing: Model automatically analyzes document layout and structure
- Dynamic chunking: Split large-sized documents into appropriately sized areas for processing
- hierarchical resolution: Recognize different elements such as text, headings, tables, charts, etc.
- contextual understanding: Extract key information by combining semantic relationships throughout the document
Useful Code Samples
image = load_image('document.jpg')
response = pipe(('提取图片中表格的内容', image))
print(response.text)Enterprise Applications
Suitable for contract analysis, invoice processing, technical document analysis and other scenarios, can significantly improve the efficiency and accuracy of document processing.
This answer comes from the articleInternVL: Open Source Multimodal Large Model with Image, Video and Text Processing SupportThe
May not be reproduced without permission:AI productivity tools " What specific features does InternVL have for document parsing? How to deal with complex documents?
