Extract text or data from PDFs

Depending on the format of your PDF (text-based vs. scanned images), there are various methods for extracting text or data from PDFs. Here are a few typical techniques:

1. Making use of PDF Processing Libraries in Python
Text can be extracted using PyPDF2, although it has trouble with intricate layouts.

More sophisticated and adept at handling structured data is pdfplumber.
PyMuPDF (fitz) is a quick and effective text extraction tool.
Tesseract OCR (Optical Character Recognition) for scanned PDFs.
Example: Using Python's PyPDF2 to extract text
PyPDF2 import in CopyEdit

using open ("document.pdf", "rb") as pdf_file: print(page.extract_text()) for page in reader. Pages: reader = PyPDF2.PdfReader(pdf_file)

 

2. Making Use of Power Automate (No-Code Method)
For structured PDFs, use AI Builder (Extract Data from Forms).
Integrate with Power Automate processes to handle and save data in Dataverse, SharePoint, or Excel.

3. Applying Layout Parsing, an Open-Source AI
For intricate designs such as forms, tables, and invoices:

Structured text can be extracted with PDFMiner.
LayoutLM (ML model): Uses AI to extract important data elements.