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PDF Data Extraction #229

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elie222 opened this issue Sep 19, 2024 · 1 comment
Open

PDF Data Extraction #229

elie222 opened this issue Sep 19, 2024 · 1 comment
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@elie222
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elie222 commented Sep 19, 2024

Description

To enhance Inbox Zero's capability in handling PDF documents, particularly receipts and potentially more complex documents like pitch decks, we need to research and implement effective PDF data extraction solutions. This will enable users to automate tasks such as sending receipt details to external services or organizing information from various types of PDFs.

Objectives

  1. Research and evaluate different PDF data extraction methods
  2. Implement a solution for simple PDF receipts using Claude LLM
  3. Explore options for handling more complex PDFs
  4. Integrate the chosen solutions with the existing AI assistant for automated document handling

Research Areas

  1. Claude LLM Capabilities:

    • Investigate how to effectively use Claude LLM for extracting data from simple PDF receipts
    • Determine the limitations and accuracy of this approach
  2. Complex PDF Handling:

    • Research methods for processing more complex PDFs (e.g., pitch decks, detailed financial reports)
    • Evaluate services like Azure AI Intelligence, considering cost-benefit trade-offs
    • Take a look at what Midday is doing for invoice extraction using Azure AI Intelligence: https://github.com/midday-ai/midday/tree/main/packages/documents
  3. Hybrid Approaches:

    • Explore possibilities of combining different methods for optimal results
    • Consider using Claude LLM for initial parsing and other tools for verification or complex cases

Implementation Steps

  1. Develop a proof of concept for using Claude LLM to extract data from simple PDF receipts
  2. Create a testing framework to assess the accuracy and reliability of the Claude LLM approach
  3. Research and potentially prototype solutions for complex PDF handling
  4. Integrate the chosen solution(s) with the AI assistant
  5. Develop user interface components for reviewing and correcting extracted data if necessary
  6. Implement error handling and fallback mechanisms

Key Considerations

  • Accuracy: Ensure high accuracy in data extraction, especially for financial documents
  • Cost: Evaluate the cost implications of different approaches, especially for third-party services

Potential Challenges

  • Handling various PDF formats and layouts
  • Balancing between using Claude LLM and other specialized tools
  • Managing costs associated with third-party services for complex PDF processing
  • Ensuring high accuracy across different types of documents

Future Directions

  • Expand capabilities to handle a wider range of document types
  • Develop more sophisticated integrations with external services based on extracted data
@MaYaNkKashyap681
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Hello @elie222, I'm interested in working on this issue. Could you please assign it to me?
Thank you!

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