Optimized Python Schema and PDF Data Extraction

📅 2024-09-17 — Session: Optimized Python Schema and PDF Data Extraction

🕒 00:00–01:17
🏷️ Labels: Python, Schema Extraction, Pdf Processing, Nosql, Data Quality
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to enhance Python functions for schema extraction and PDF data processing, focusing on optimizing code logic and ensuring accurate data handling.

Key Activities

• Traversal Logic Fix: Addressed issues in the extract_parameters function to preserve nested key structures, ensuring relevant keys maintain their full hierarchy.
• Schema Extraction Optimization: Implemented a simplified approach to extract top-level properties while retaining nested structures, avoiding recursion.
• Debugging: Resolved a TypeError in dictionary handling and corrected dictionary assignment issues in schema extraction.
• Data Formatting: Fixed JSON output formatting in Pandas to ensure valid JSON arrays.
• PDF Data Extraction: Explored methods for extracting non-selectable text from PDFs using PyMuPDF and Tesseract OCR, including installation guidance and troubleshooting.
• NoSQL Data Processing: Planned strategies for leveraging NoSQL data, focusing on quality assessment and strategic data utilization.

Achievements

• Successfully optimized schema extraction logic and fixed JSON formatting issues.
• Enhanced PDF data extraction capabilities using OCR technologies.
• Developed strategic plans for NoSQL data utilization and quality assessment.

Pending Tasks

• Further testing and validation of the optimized schema extraction functions.
• Implementation of NoSQL data strategies in live environments.
• Continued refinement of PDF data extraction techniques.