Upstage AI Document Parser - Revolutionise Complex PDF Data Extraction!

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AI Summary

Summary of Video Transcript

• LMS Document Reading Capabilities
  • Can read documents quickly and accurately.
  • Supports conversion to text, HTML, and Markdown.
  • Handles various document types: PDF, JPEG, BMP, DOCX, XLSX, PPTX.
• Performance Comparison
  • Faster parsing than Azure AI, Llama PA, Amazon Textract, and Unstructured.
  • Maintains speed with an increasing number of pages.
  • More accurate in text and table structure recognition compared to competitors.
• Benchmark Metrics
  • Traditional metrics are insufficient for hierarchical table structures.
  • TEDS and TEDS-S measure similarity between predicted and actual tables.
  • Normalized Indel Distance evaluates serialization of document elements.
• Layout Categorization and HTML Extraction
  • Categorizes layouts in human reading order with different colors.
  • Converts images to LaTeX format for equations.
  • Provides coordinates for bounding boxes of tables, images, and text.
• Document Parsing Benchmark (DP Bench)
  • Upstage released DP Bench for element detection and table structure recognition.
  • Scripts and datasets for testing are provided.
• Instructions for Running Benchmarks
  • Clone the repository with git clone [repo URL].
  • Navigate to the scripts and dataset folders.
  • Install dependencies with pip install.
  • Set environment variables for API keys and endpoints.
  • Run parsing scripts for Llama PA and Upstage.
  • Evaluate results with provided evaluation script.
• Integration into Applications
  • Demonstrates parsing a complex PDF document.
  • Provides a sample code snippet for integration.
  • Results include detailed sections with coordinates and types.
• Testing and Deployment
  • Users can test the document parser in the Upstage playground.
  • The parser can be integrated into applications and deployed on user infrastructure.
• Further Learning
  • Encourages learning about language models’ capabilities in analyzing images.

Detailed Instructions and URLs

• Repository Cloning
  • Command: git clone [repo URL]
• Benchmark Scripts and Datasets
  • Key folders: scripts and datasets
• Dependency Installation
  • Command: pip install markdown requests beautifulsoup4
• Setting Environment Variables
  • Commands:
    • export LLAMA_PASS_GET_URL=[URL]
    • export LLAMA_PASS_POST_URL=[URL]
    • export LLAMA_PASS_API_KEY=[API key]
    • export UPSTAGE_ENDPOINT=[URL]
    • export UPSTAGE_API_KEY=[API key]
• Running Parsing Scripts
  • Commands:
    • Llama PA: python infer_llama_pass.py [PDFs path] [save path]
    • Upstage: python infer_upstage.py [PDFs path] [save path]
• Evaluation of Results
  • Command: python evaluate.py [reference path] [prediction path]
• Integration Code Snippet
  • Sample code provided for integrating the document parser into an application.
• Playground and API Key
  • Upstage playground URL: console.upstage.doai
  • API key generation is done through the console.

Please note that the exact URLs and some specific commands were not provided in the transcript, hence they are represented as placeholders [URL], [PDFs path], [save path], [API key], [reference path], and [prediction path].