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, Excel, PowerPoint.
• Performance Comparison
  • Faster parsing than competitors: Azure AI, Llama PA, Amazon Textract, Unstructured.
  • Maintains high speed with increased page numbers.
  • More accurate in text and table structure recognition compared to Amazon, Llama PA, Unstructured, Google, Azure.
  • Average parsing time: 3.79 seconds.
• Evaluation Metrics
  • TEDS and TEDS-S: Measure similarity between predicted and actual tables, considering layout and content.
  • Normalized Indel Distance: Assesses detection and serialization of document elements based on human reading order.
  • Layout categorization: Categorizes layouts in human reading order with different colors.
  • HTML extraction: Identifies lists, tables, headings, etc.
• Document Parsing Benchmark (DP Bench)
  • Allows users to verify results and integrate with their own applications.
  • Upstage has released DP Bench focusing on element detection, serialization, and table structure recognition.
  • Provides scripts and datasets for testing.
• Using Upstage Playground
  • URL: console.upstage.doai
  • Users can upload files and identify elements, even extracting data from images.
  • Converts images to LaTeX format and provides coordinates for bounding boxes.
• Running DP Bench Locally
  • Clone the repository with git clone and the repo URL.
  • Navigate to the scripts and dataset folders.
  • Install necessary Python packages.
  • Set environment variables for API keys and endpoints.
  • Run parsing scripts for Llama PA and Upstage, saving results to JSON files.
  • Evaluate results using evaluate.py script, comparing NID scores.
• Integrating Document Parser in Applications
  • Export Upstage API key from console.upstage.doai.
  • Create a script to parse a complex PDF document.
  • Use Python’s requests library to post to the parsing URL and receive a response.
  • The response includes detailed information about each document section.
• Conclusion
  • Upstage’s document parser is highlighted as superior in speed and accuracy.
  • The video demonstrates how to run benchmarks and integrate the parser into applications.
  • Encourages viewers to learn more about language models and image analysis.

Detailed Instructions and URLs

• Upstage Playground URL: console.upstage.doai
• Repository Cloning: git clone [repo URL]
• Environment Variables: Set for Llama PA and Upstage API keys and endpoints.
• Running Scripts: Use Python to execute parsing and evaluation scripts.
• API Key Export: Export Upstage API key from the console URL provided.
• Script Creation: Write a Python script to parse documents using Upstage’s API.

(Note: Specific CLI commands, URLs, and API keys are not provided in the summary as they were not included in the provided text.)