1. Background
In daily work, we often need to process PDF files, such as extracting text content, analyzing document structure, etc. However, the format of PDF files is complex, and it is not easy to extract information directly. The pdfminer library came into being. It can efficiently parse PDF files, extract text, metadata, tables and other information, helping us easily meet various PDF processing needs. Next, let's dive into this powerful tool.
2. What is pdfminer
pdfminer is an open source third-party Python library designed for parsing PDF files. It provides a rich API that can accurately extract text, analyze page layout, extract metadata, etc. Its core function is to convert the content of PDF files into actionable text data for further processing and analysis.
3. How to install pdfminer
pdfminer is a third-party library that can be installed via the following command line:
pip install
After the installation is completed, you can confirm whether the installation is successful through the following command:
python -c "import pdfminer; print(pdfminer.__version__)"
If the version number can be output normally, the installation is successful.
4. Simple library function usage method
The following are five commonly used functions in pdfminer and their usage methods:
1. Extract text
from pdfminer.high_level import extract_text
text = extract_text("")
print(text)
The extract_text function is used to extract all text from a PDF file.
2. Obtain page layout information
from import LAParams, LTTextBox, LTTextLine
from import PDFPage
from import PDFResourceManager, PDFPageInterpreter
from import PDFPageAggregator
resource_manager = PDFResourceManager()
fake_file_handle = ()
converter = PDFPageAggregator(resource_manager, laparams=LAParams())
page_interpreter = PDFPageInterpreter(resource_manager, converter)
with open("", "rb") as pdf_file:
for page in PDFPage.get_pages(pdf_file):
page_interpreter.process_page(page)
layout = converter.get_result()
for lt_obj in layout:
if isinstance(lt_obj, (LTTextBox, LTTextLine)):
text = lt_obj.get_text()
x, y, width, height = lt_obj.bbox
font = lt_obj._objs[0].fontname
font_size = lt_obj._objs[0].size
print(f"Text: {()}, Position: ({x:.2f}, {y:.2f}), Font: {font}, Size: {font_size:.2f}")This code obtains information such as the location, font and font size of the text block.
3. Extract table data
from pdfminer.high_level import extract_text
import tabula
table_text = extract_text("table_example.pdf")
print(table_text)
tables = tabula.read_pdf("table_example.pdf", pages="all")
for df in tables:
print(df)
Use pdfminer to extract tables in PDF documents and use tabula to extract table data.
4. Extract images
from import PDFParser
from import PDFDocument
from import PDFStream
import io
from PIL import Image
with open('', 'rb') as file:
parser = PDFParser(file)
document = PDFDocument(parser)
if document.is_extractable:
for xref in :
if xref.get_subtype() == '/Image':
stream_obj = xref.get_object()
if isinstance(stream_obj, PDFStream):
data = stream_obj.get_rawdata()
image = ((data))
()Extract images from PDF documents.
5. Extract metadata
from import PDFParser
from import PDFDocument
def extract_metadata(pdf_path):
with open(pdf_path, 'rb') as fh:
parser = PDFParser(fh)
doc = PDFDocument(parser)
metadata = [0]
for key, value in ():
print(f"{key}: {value}")
extract_metadata('')
Extract the metadata of the PDF file.
5. Practical application scenarios
The following are examples of application of pdfminer in different scenarios:
1. Legal Document Processing
from pdfminer.high_level import extract_text
def extract_legal_document_text(pdf_path):
text = extract_text(pdf_path)
return text
text = extract_legal_document_text('legal_document.pdf')
print(text)
In the legal industry, text and metadata in legal documents are extracted and analyzed through pdfminer to automatically generate reports.
2. Financial statement analysis
from import LAParams, LTTextBoxHorizontal
from import PDFPage
from import PDFResourceManager, PDFPageInterpreter
from import PDFPageAggregator
def extract_financial_tables(pdf_path):
with open(pdf_path, 'rb') as fh:
rsrcmgr = PDFResourceManager()
laparams = LAParams()
device = PDFPageAggregator(rsrcmgr, laparams=laparams)
interpreter = PDFPageInterpreter(rsrcmgr, device)
for page in PDFPage.get_pages(fh, caching=True, check_extractable=True):
interpreter.process_page(page)
layout = device.get_result()
for element in layout:
if isinstance(element, LTTextBoxHorizontal):
print(element.get_text())
extract_financial_tables('financial_report.pdf')In the financial industry, the tabular data in financial statements are extracted through pdfminer for automated data analysis and processing.
3. Research paper data extraction
from import LAParams, LTTextBoxHorizontal, LTFigure
from import PDFPage
from import PDFResourceManager, PDFPageInterpreter
from import PDFPageAggregator
def extract_research_paper_content(pdf_path):
with open(pdf_path, 'rb') as fh:
rsrcmgr = PDFResourceManager()
laparams = LAParams()
device = PDFPageAggregator(rsrcmgr, laparams=laparams)
interpreter = PDFPageInterpreter(rsrcmgr, device)
for page in PDFPage.get_pages(fh, caching=True, check_extractable=True):
interpreter.process_page(page)
layout = device.get_result()
for element in layout:
if isinstance(element, LTTextBoxHorizontal):
print(element.get_text())
elif isinstance(element, LTFigure):
print("Figure found")
extract_research_paper_content('research_paper.pdf')In academic research, text and chart information from research papers are extracted through PDFminer to assist in research analysis.
4. Text page-by-page extraction
from import PDFPage
from import PDFResourceManager, PDFPageInterpreter
from import TextConverter
from io import StringIO
def extract_text_by_page(pdf_path):
resource_manager = PDFResourceManager()
fake_file_handle = StringIO()
converter = TextConverter(resource_manager, fake_file_handle)
page_interpreter = PDFPageInterpreter(resource_manager, converter)
with open(pdf_path, 'rb') as fh:
for page in PDFPage.get_pages(fh, caching=True, check_extractable=True):
page_interpreter.process_page(page)
text = fake_file_handle.getvalue()
yield text
()
fake_file_handle.close()
for page_text in extract_text_by_page(''):
print(page_text)Extract text from PDF files page by page, which is suitable for situations where page by page.
5. Extract the directory
from import PDFParser
from import PDFDocument, PDFNoOutlines
def extract_toc(pdf_path):
with open(pdf_path, 'rb') as file:
parser = PDFParser(file)
document = PDFDocument(parser)
try:
outlines = document.get_outlines()
toc = []
for (level, title, dest, a, se) in outlines:
((level, title))
return toc
except PDFNoOutlines:
return []
toc = extract_toc('')
for item in toc:
print(f"Level: {item[0]}, Title: {item[1]}")Extract the directory of PDF documents to facilitate and quickly locate the document structure.
6. Frequently Asked Questions and Solutions
The following are common problems and solutions when using pdfminer:
Text extraction is empty
Error message: extract_text returns an empty string.
Cause: A PDF file may contain non-text content, or the text is embedded as an image.
Solution: Check the contents of the PDF file to make sure the text is extractable. If the text is embedded as an image, you can try using an OCR tool (such as `pytesseract
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