: Use tools like pdfplumber to visualize what the code "sees" before processing.

Complex documents requiring "reasoning" to understand context (e.g., invoices). ⚠️ Key Challenges

: Scanned or skewed pages can lead to high error rates in OCR.

: Separate extraction from transformation so you can re-run cleaning logic without re-parsing the file.

: Data often looks like a table but is actually just floating text.

Developers needing granular control over text and table coordinates. Tesseract , Amazon Textract , Azure AI Document Intelligence Scanned documents or images where text isn't selectable. Modern AI ChatGPT (as OCR) , LangChain

: Standard parsers may read across columns instead of down them.

: Sending the structured data into a final destination like a PostgreSQL database , Amazon S3 , or a Snowflake data warehouse . 🛠️ Common Tools for PDF Extraction Tool Category Python Libraries PyMuPDF , Tabula-py , pdfplumber