Animal 911 Calls Extraction from Rainforest Cafe Report
This PDF is a service call report covering 911 incidents at the Rainforest Cafe in Niagara Falls, NY. We're hunting for animals! The data is formatted as a spreadsheet within the PDF, and challenges include varied column widths, borderless tables, and large swaths of missing data.
from natural_pdf.analyzers.guides import Guides
guide = Guides(pages[0])
columns = ['Number', 'Date Occurred', 'Time Occurred', 'Location', 'Call Type', 'Description', 'Disposition', 'Main Officer']
guide.vertical.from_content(columns, outer="last")
guide.horizontal.from_content(from natural_pdf.analyzers.guides import Guides
base = Guides(pages[0])
columns = ['Number', 'Date Occurred', 'Time Occurred', 'Location', 'Call Type', 'Description', 'Disposition', 'Main Officer']
base.vertical.from_content(columns, outer="last")
base.horizontal.from_content(pages[0].find_all('text:starts-with(NF-)'))dataframes = [first_table]
for page in pages:
guides = Guides(page)
guides.vertical = base.vertical
guides.horizontal.from_content(page.find_all('text:starts-with(NF-)'))
single_df = guides.extract_table().to_df(header=columns)Complex Extraction of Law Enforcement Complaints
This PDF contains a set of complaint records from a local law enforcement agency. Challenges include its relational data structure, unusual formatting common in the region, and redactions that disrupt automatic parsing.
)
# Build vertical guidelines from lines
guides = Guides(table)
guides.vertical.from_lines(n=4)
# Use the guides .expand(top=5, bottom=7)
)
guides = Guides(table)
guides.vertical.from_lines(n=8)
( )
# Use the guides to extract the table
guides = Guides(table)
guides.vertical.from_lines(n=8)
columns = ['Name', 'ID No.', 'Rank', 'Division', 'Officer Disposition', 'Action Taken', 'Body Cam']
officer_df = (Extracting State Agency Call Center Wait Times from FOIA PDF
This PDF contains data on wait times at a state agency call center. The main focus is on the data on the first two pages, which matches other states' submission formats. The later pages provide granular breakdowns over several years. Challenges include it being heavily pixelated, making it hard to read numbers and text, with inconsistent and unreadable charts.
from natural_pdf.analyzers.guides import Guides
guide = Guides(table_area)
guide.vertical.divide(3)
guide.vertical.snap_to_whitespace(detection_method='text')
guide.horizontal.from_lines()OCR and AI magic
Master OCR techniques with Natural PDF - from basic text recognition to advanced LLM-powered corrections. Learn to extract text from image-based PDFs, handle tables without proper boundaries, and leverage AI for accuracy improvements.
from natural_pdf.analyzers import Guides
guides = Guides(table_area)
# Add guides between the headers
guides.vertical.from_content(Working with page structure
Extract text from complex multi-column layouts while maintaining proper reading order. Learn techniques for handling academic papers, newsletters, and documents with intricate column structures using Natural PDF's layout detection features.
from natural_pdf.analyzers import Guides
guides = Guides(table_area)
guides.vertical.from_lines(threshold=0.6)
guides.horizontal.from_lines(threshold=0.6)
guides.show()