Entity Extractor Pro Extract and visualize named entities from PDF documents using Python's SpaCy and PyMuPDF.

📋 Project Overview Entity Extractor Pro is a Python-based project designed to extract named entities from PDF documents and visualize them using SpaCy. The tool reads through each page of a PDF, processes the text for entity recognition, and allows interactive visualization of the results.

🚀 Features Extracts named entities such as names, dates, organizations, and more from PDF files. Utilizes SpaCy's powerful en_core_web_lg model for accurate entity recognition. Processes multi-page PDFs seamlessly. Provides an intuitive visualization of entities using SpaCy's DisplaCy.

🛠️ Technologies Used Python: Main programming language. SpaCy: For natural language processing and named entity recognition. PyMuPDF (fitz): For reading and extracting text from PDF documents. python-docx: (Optional) Future scope to handle DOCX files for similar processing.

🗂️ Project Structure Input: A user-provided PDF file. Process: Extract text from each page using PyMuPDF. Apply SpaCy's NLP pipeline to identify named entities. Store extracted entities in a structured format. Output: A list of named entities with their types. Interactive visualization of entities in Jupyter Notebook using DisplaCy.

🧑‍💻 How to Use Install Required Libraries: pip install spacy PyMuPDF python-docx python -m spacy download en_core_web_lg Run the Script: Save the code as entity_extractor.py. Execute the script in a Python environment: python entity_extractor.py Enter the path to the PDF file when prompted. Visualize Results: Open the output in a Jupyter Notebook to view the entity visualization using DisplaCy.

🤝 Contributing Contributions are welcome! Feel free to fork the repository and submit a pull request with your improvements.