Detailed tutorial on using PDF parsing tool pdfplumber in Python

pip install pdfplumber

import pdfplumber

with ("") as pdf:
    first_page = [0]
    print(first_page.extract_text())

with ("") as pdf:
    for page in :
        print(page.extract_text())

with ("") as pdf:
    page = [0]
    words = page.extract_words()
    for word in words:
        print(f"text: {word['text']}, Location: {word['x0'], word['top']}, Font: {word['fontname']}")

with ("") as pdf:
    page = [0]
    # Define the area (x0, top, x1, bottom)    area = (50, 100, 400, 300)  
    cropped = (area)
    print(cropped.extract_text())

with ("") as pdf:
    page = [0]
    table = page.extract_table()
    for row in table:
        print(row)

with ("complex_table.pdf") as pdf:
    page = [0]
    # Customize table settings    table_settings = {
        "vertical_strategy": "text", 
        "horizontal_strategy": "text",
        "intersection_y_tolerance": 10
    }
    table = page.extract_table(table_settings)

with ("multi_page_table.pdf") as pdf:
    full_table = []
    for page in :
        table = page.extract_table()
        if table:
            # Skip the table header (assuming that there is already a table header on the first page)            if page.page_number > 1:
                table = table[1:]
            full_table.extend(table)
    
    for row in full_table:
        print(row)

with ("") as pdf:
    page = [0]
    im = page.to_image()
    im.debug_tablefinder().show()

with ("") as pdf:
    page = [0]
    lines = 
    curves = 
    rects = 
    
    print(f"turn up {len(lines)} A straight line")
    print(f"turn up {len(curves)} Curve")
    print(f"turn up {len(rects)} Rectangles")

def custom_extract_method(page):
    # Get all character objects    chars = 
    # Group by y coordinate (row)    lines = {}
    for char in chars:
        line_key = round(char["top"])
        if line_key not in lines:
            lines[line_key] = []
        lines[line_key].append(char)
    
    # Sort by x coordinates and splice text    result = []
    for y in sorted(()):
        line_chars = sorted(lines[y], key=lambda c: c["x0"])
        line_text = "".join([c["text"] for c in line_chars])
        (line_text)
    
    return "\n".join(result)

​​​​​​​with ("") as pdf:
    page = [0]
    print(custom_extract_method(page))

with ("") as pdf:
    # Only process the first 5 pages    for page in [:5]:
        process(page.extract_text())

from  import ThreadPoolExecutor

def process_page(page):
    return page.extract_text()

with ("big_file.pdf") as pdf:
    with ThreadPoolExecutor(max_workers=4) as executor:
        results = list((process_page, ))

import pickle

def extract_and_cache(pdf_path, cache_path):
    try:
        with open(cache_path, "rb") as f:
            return (f)
    except FileNotFoundError:
        with (pdf_path) as pdf:
            data = [page.extract_text() for page in ]
            with open(cache_path, "wb") as f:
                (data, f)
            return data

text_data = extract_and_cache("", "report_cache.pkl")

def extract_invoice_info(pdf_path):
    invoice_data = {
        "invoice_no": None,
        "date": None,
        "total": None
    }
    
    with (pdf_path) as pdf:
        for page in :
            text = page.extract_text()
            lines = ("\n")
            
            for line in lines:
                if "Invoice number" in line:
                    invoice_data["invoice_no"] = (":")[1].strip()
                elif "date" in line:
                    invoice_data["date"] = (":")[1].strip()
                elif "total" in line:
                    invoice_data["total"] = ()[-1]
    
    return invoice_data

def analyze_paper(pdf_path):
    sections = {
        "abstract": "",
        "introduction": "",
        "conclusion": ""
    }
    
    with (pdf_path) as pdf:
        current_section = None
        for page in :
            text = page.extract_text()
            for line in ("\n"):
                line = ()
                if () == "abstract":
                    current_section = "abstract"
                elif ().startswith("1. introduction"):
                    current_section = "introduction"
                elif ().startswith("conclusion"):
                    current_section = "conclusion"
                elif current_section:
                    sections[current_section] += line + "\n"
    
    return sections

import csv

def convert_pdf_to_csv(pdf_path, csv_path):
    with (pdf_path) as pdf:
        with open(csv_path, "w", newline="") as f:
            writer = (f)
            for page in :
                table = page.extract_table()
                if table:
                    (table)

with ("") as pdf:
    page = [0]
    # Make sure the system has Chinese fonts installed    text = page.extract_text()
    print(("utf-8").decode("utf-8"))

table_settings = {
    "vertical_strategy": "lines",
    "horizontal_strategy": "lines",
    "explicit_vertical_lines": ,
    "explicit_horizontal_lines": ,
    "intersection_x_tolerance": 15,
    "intersection_y_tolerance": 15
}
table = page.extract_table(table_settings)

# Process page by page and release memory immediatelywith ("") as pdf:
    for i, page in enumerate():
        process(page.extract_text())
        # Manually release page resources        pdf.release_resources()
        if i % 10 == 0:
            print(f"Processed {i+1} Page")

# Optimize PDF using Ghostscriptimport subprocess
(["gs", "-sDEVICE=pdfwrite", "-dNOPAUSE", "-dBATCH", 
               "-dSAFER", "-sOutputFile=", ""])

# Combine PyMuPDF to get more accurate text locationimport fitz
doc = ("")

def safe_extract(pdf_path):
    try:
        with (pdf_path) as pdf:
            return [0].extract_text()
    except Exception as e:
        print(f"deal with{pdf_path}An error occurred while: {str(e)}")
        return None

import time
start = ()
# pdf processing operationsprint(f"Time-consuming processing: {()-start:.2f}Second")