Extracting Text from Georgia Legislative Bills
This PDF contains legal bills from the Georgia legislature, published yearly. Challenges include extracting marked-up text like underlines and strikethroughs. It has line numbers that complicate text extraction.
text = pdf.find_all('text:underline').extract_each_text()
print(text)sections.extract_each_text()Extracting Use-of-Force Records from Vancouver Police PDF
This PDF contains detailed records of Vancouver Police's use-of-force incidents, provided after a public records request by journalists. Challenges include its very very very small font size and lots of empty whitespace.
headers = page.find_all('text[y0=min()]')
headers.extract_each_text()Natural PDF basics with text and tables
Learn the fundamentals of Natural PDF - opening PDFs, extracting text with layout preservation, selecting elements by criteria, spatial navigation, and managing exclusion zones. Perfect starting point for PDF data extraction.
texts = page.find_all('text').extract_each_text()
for t in texts[:5]: # Show first 5
print(t)