right - Method Usage

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.

complainant = (
  section
  .find("text:contains(Complainant)")
  .right(until='text')
)
print("Complainant is", complainant.extract_text())
complainant.show(crop=100)

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dob = (
  section
  .find("text:contains(DOB)")
  .right(until='text')
)
print("DOB is", dob.extract_text())
dob.show(crop=100)

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complainant = (
  section
  .find("text:contains(Complainant)")
  .right(until='text')
)
dob = (
  section

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dob = (
  section
  .find("text:contains(DOB)")
  .right(until='text')
)
address = (
  section

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address = (
  section
  .find("text:contains(Address)")
  .right(until='text')
)
gender = (
  section

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gender = (
  section
  .find("text:contains(Gender)")
  .right(until='text')
)
phone = (
  section

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phone = (
  section
  .find("text:contains(H Phone)")
  .right(until='text')
)
date_assigned = (
  section

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(
    section
    .find_all('text:contains(Complaint #)')
    .right(include_source=True)
    .show(crop=section)
)

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(
    section
    .find_all('text:contains(Complaint #)')
    .right(include_source=True)
    .merge()
    .expand(top=5, bottom=7)
    .show(crop=section)

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(
    section
    .find_all('text:contains(Complaint #)')
    .right(include_source=True)
    .merge()
    .expand(top=5, bottom=7)
    .extract_table()

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table = (
    section
    .find_all('text:contains(Complaint #)')
    .right(include_source=True)
    .merge()
    .expand(top=5, bottom=7)
)

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table_area = (
    section
    .find_all('text:contains(Officer #)')
    .right(include_source=True)
    .merge()
    .expand(top=5, bottom=7)
)

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rows = []
for section in sections:
    complainant = section.find("text:contains(Complainant)").right(until='text')
    dob = section.find("text:contains(DOB)").right(until='text')
    address = section.find("text:contains(Address)").right(until='text')
    gender = section.find("text:contains(Gender)").right(until='text')

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rows = []
for section in sections:
    complainant = section.find("text:contains(Complainant)").right(until='text')
    dob = section.find("text:contains(DOB)").right(until='text')
    address = section.find("text:contains(Address)").right(until='text')
    gender = section.find("text:contains(Gender)").right(until='text')
    phone = section.find("text:contains(H Phone)").right(until='text')

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for section in sections:
    complainant = section.find("text:contains(Complainant)").right(until='text')
    dob = section.find("text:contains(DOB)").right(until='text')
    address = section.find("text:contains(Address)").right(until='text')
    gender = section.find("text:contains(Gender)").right(until='text')
    phone = section.find("text:contains(H Phone)").right(until='text')
    investigator = (

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    complainant = section.find("text:contains(Complainant)").right(until='text')
    dob = section.find("text:contains(DOB)").right(until='text')
    address = section.find("text:contains(Address)").right(until='text')
    gender = section.find("text:contains(Gender)").right(until='text')
    phone = section.find("text:contains(H Phone)").right(until='text')
    investigator = (
        section

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    dob = section.find("text:contains(DOB)").right(until='text')
    address = section.find("text:contains(Address)").right(until='text')
    gender = section.find("text:contains(Gender)").right(until='text')
    phone = section.find("text:contains(H Phone)").right(until='text')
    investigator = (
        section
        .find("text:contains(Investigator)")

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    table = (
        section
        .find_all('text:contains(Officer #)')
        .right(include_source=True)
        .merge()
        .expand(top=3, bottom=6)
    )

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Extracting Economic Data from Brazil's Central Bank PDF

This PDF is the weekly “Focus” report from Brazil’s central bank with economic projections and statistics. Challenges include commas instead of decimal points, images showing projection changes, and tables without border lines that merge during extraction.

(
    data
    .find('text:contains(2025)')
    .right(
        until='text:contains(2026)',
        include_source=True,
        include_endpoint=False

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table = (
    data
    .find('text:contains(2025)')
    .right(
        until='text:contains(2026)',
        include_source=True,
        include_endpoint=False

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table = (
    data
    .find('text:contains(2026)')
    .right(
        until='text:contains(2027)',
        include_source=True,
        include_endpoint=False

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table = (
    data
    .find('text:contains(2027)')
    .right(
        until='text:contains(2028)',
        include_source=True,
        include_endpoint=False

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table = (
    data
    .find('text:contains(2028)')
    .right(include_source=True)
    .below(width='element')
    .expand(top=-20)
    .clip(data)

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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.

  page
  .region(right=70)
  .find_all('text')
  .right()
  .show(crop='wide')
)

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  page
  .region(right=70)
  .find_all('text')
  .right()
  .merge()
  .show(crop='wide')
)

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    page
        .region(right=70)
        .find_all('text')
        .right()
        .merge()
    )
)

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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.

# Extract text to the right of "Date:"
date = page.find(text="Date:").right(height='element')
date.show()

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