Python OCR and Vision AI Automation

Sep 8, 2024

Python RPA Automation Series Lecture Notes

Introduction

  • Welcome to the Python RPA Automation Series
  • Covering the use of LLMA models and model weights on different operating systems
    • Previous videos: Ubuntu setup, Windows documentation
    • Use case demonstration: Monitoring employee attendance and expenses

Today's Use Case: OCR and Vision AI

  • Objective: Build an inexpensive OCR and Vision AI for real-life data
  • Steps of the process:
    1. Employee submits expense sheet or receipt
    2. Automate reading content from document/image
    3. Example: Reading stock information from a screenshot

Demonstration of the Application

  • Using a complex document (e.g., screenshot of stock prices)
  • Example: Extracting average volume of a stock (e.g., Apple) from the text
  • Results: Ability to dynamically create a JSON object from extracted data

Introduction of the Speaker

  • Name: Amish Shukla
  • Background: Training neural networks in finance, supply chain, healthcare
  • Goal: To predict useful patterns, especially in supply chain shortages during the pandemic

Why Develop a New OCR Solution?

  • Many existing solutions are wrappers around open-source packages
  • Current market solutions are often expensive and not trained on specific data
  • Custom training on in-house data can yield significantly better results (10-100x improvement)
  • Possible use cases: Document classification, digital signatures, confidential information scanning

Code Walkthrough

  1. File Handling: Automate actions upon file receipt
    • Example: File uploaded via FTP
    • Linux code for file placement
  2. Screenshot Capture: Use Pillow library to capture screenshots
    • Example: Taking a screenshot of a webpage and saving it as apple.png
  3. Text Extraction: Using PI Tesseract to read text from images
    • Function to extract text from apple.png
  4. Building Dynamic Prompts: Creating prompts for language models
    • Example prompts: "What is the average volume of stock in this text?"
  5. Using Language Models: Calling LLMA and ChatGPT to process prompts
    • Demonstration of extracting specific stock information

Prompt Engineering

  • Importance of creative and relevant prompts
  • Experiment with varying the questions to improve extraction accuracy
  • Real-time demonstration of LLMA and ChatGPT performance

Conclusion

  • Results from the demonstration:
    • Accurate responses and formatted outputs from the models
    • Advantages of using in-house trained models over generic solutions
  • Encouragement to explore the full code and repository for more details

Call to Action

  • Questions? Open issues on GitHub repository
  • Subscribe to the channel for more updates
  • Thank you for watching!