Project 1: How AI Reads Pharmaceutical Documents

I analyzed how AI interprets pharmaceutical documents by investigating Machine Learning, Deep Learning, LLMs, and NLP. This involved evaluating a real pharmaceutical documentation file and assessing AI models’ capabilities in processing both structured and unstructured data.

Project 3: Data Extraction from Documents Using Python

I converted digital PDFs into machine-readable formats using Python, employing tools like PyMuPDF and pdfplumber. I applied field heuristics, including regex patterns and anchor phrases, to identify key fields such as dates and vendor names, laying the foundation for Pfizer's compliance workflow.

Project 2: Learn to Work with Python for AI-Powered Document Processing

I utilized Python for data processing, cleaning, and organizing pharmaceutical documents, preparing them for AI automation. This involved exploring Google Colab, mastering Python basics, and applying data preparation techniques to clean and standardize information, ultimately enhancing OCR accuracy.

Project 4: Advanced OCR Comparison and Layout-Aware Extraction

I conducted a hands-on comparison of three OCR engines—Tesseract, PaddleOCR, and EasyOCR—on scanned pharmaceutical supplier documentation. I learned to clean and extract text from scanned PDFs and explored layout-aware tools, ultimately recommending the most effective OCR tool based on technical