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# To read the PDF
import PyPDF2
# To analyze the PDF layout and extract text
from pdfminer.high_level import extract_pages, extract_text
from pdfminer.layout import LTTextContainer, LTChar, LTRect, LTFigure
# To extract text from tables in PDF
import pdfplumber
# To extract the images from the PDFs
from PIL import Image
from pdf2image import convert_from_path
# To perform OCR to extract text from images
import pytesseract
# To remove the additional created files
import os
def text_extraction(element):
# Extracting the text from the in-line text element
line_text = element.get_text()
# Find the formats of the text
# Initialize the list with all the formats that appeared in the line of text
line_formats = []
for text_line in element:
if isinstance(text_line, LTTextContainer):
# Iterating through each character in the line of text
for character in text_line:
if isinstance(character, LTChar):
# Append the font name of the character
line_formats.append(character.fontname)
# Append the font size of the character
line_formats.append(character.size)
# Find the unique font sizes and names in the line
format_per_line = list(set(line_formats))
# Return a tuple with the text in each line along with its format
return (line_text, format_per_line)
def crop_image(element, pageObj):
# Get the coordinates to crop the image from the PDF
[image_left, image_top, image_right, image_bottom] = [element.x0,element.y0,element.x1,element.y1]
# Crop the page using coordinates (left, bottom, right, top)
pageObj.mediabox.lower_left = (image_left, image_bottom)
pageObj.mediabox.upper_right = (image_right, image_top)
# Save the cropped page to a new PDF
cropped_pdf_writer = PyPDF2.PdfWriter()
cropped_pdf_writer.add_page(pageObj)
# Save the cropped PDF to a new file
with open('cropped_image.pdf', 'wb') as cropped_pdf_file:
cropped_pdf_writer.write(cropped_pdf_file)
# Create a function to convert the PDF to images
def convert_to_images(input_file,):
images = convert_from_path(input_file,poppler_path=r'C:\Program Files\poppler-23.08.0\Library\bin')
image = images[0]
output_file = "PDF_image.png"
image.save(output_file, "PNG")
# Create a function to read text from images
def image_to_text(image_path):
# Read the image
img = Image.open(image_path)
# Extract the text from the image
text = pytesseract.image_to_string(img)
return text
def extract_table(pdf_path, page_num, table_num):
# Open the pdf file
pdf = pdfplumber.open(pdf_path)
# Find the examined page
table_page = pdf.pages[page_num]
# Extract the appropriate table
table = table_page.extract_tables()[table_num]
return table
# Convert table into the appropriate format
def table_converter(table):
table_string = ''
# Iterate through each row of the table
for row_num in range(len(table)):
row = table[row_num]
# Remove the line breaker from the wrapped texts
cleaned_row = [item.replace('\n', ' ') if item is not None and '\n' in item else 'None' if item is None else item for item in row]
# Convert the table into a string
table_string+=('|'+'|'.join(cleaned_row)+'|'+'\n')
# Removing the last line break
table_string = table_string[:-1]
return table_string
def pdf_manager(pdf_path):
# create a PDF file object
pdfFileObj = open(pdf_path, 'rb')
# create a PDF reader object
pdfReaded = PyPDF2.PdfReader(pdfFileObj)
# Create the dictionary to extract text from each image
text_per_page = {}
# We extract the pages from the PDF
for pagenum, page in enumerate(extract_pages(pdf_path)):
# Initialize the variables needed for the text extraction from the page
pageObj = pdfReaded.pages[pagenum]
page_text = []
line_format = []
text_from_images = []
text_from_tables = []
page_content = []
# Initialize the number of the examined tables
table_num = 0
first_element= True
table_extraction_flag= False
# Open the pdf file
pdf = pdfplumber.open(pdf_path)
# Find the examined page
page_tables = pdf.pages[pagenum]
# Find the number of tables on the page
tables = page_tables.find_tables()
# Find all the elements
page_elements = [(element.y1, element) for element in page._objs]
# Sort all the elements as they appear in the page
page_elements.sort(key=lambda a: a[0], reverse=True)
# Find the elements that composed a page
for i,component in enumerate(page_elements):
# Extract the position of the top side of the element in the PDF
pos= component[0]
# Extract the element of the page layout
element = component[1]
# Check if the element is a text element
if isinstance(element, LTTextContainer):
# Check if the text appeared in a table
if table_extraction_flag == False:
# Use the function to extract the text and format for each text element
(line_text, format_per_line) = text_extraction(element)
# Append the text of each line to the page text
page_text.append(line_text)
# Append the format for each line containing text
line_format.append(format_per_line)
page_content.append(line_text)
else:
# Omit the text that appeared in a table
pass
# Check the elements for images
if isinstance(element, LTFigure):
# Crop the image from the PDF
crop_image(element, pageObj)
# Convert the cropped pdf to an image
convert_to_images('cropped_image.pdf')
# Extract the text from the image
image_text = image_to_text('PDF_image.png')
text_from_images.append(image_text)
page_content.append(image_text)
# Add a placeholder in the text and format lists
page_text.append('image')
line_format.append('image')
# Check the elements for tables
if isinstance(element, LTRect):
# If the first rectangular element
if first_element == True and (table_num+1) <= len(tables):
# Find the bounding box of the table
lower_side = page.bbox[3] - tables[table_num].bbox[3]
upper_side = element.y1
# Extract the information from the table
table = extract_table(pdf_path, pagenum, table_num)
# Convert the table information in structured string format
table_string = table_converter(table)
# Append the table string into a list
text_from_tables.append(table_string)
page_content.append(table_string)
# Set the flag as True to avoid the content again
table_extraction_flag = True
# Make it another element
first_element = False
# Add a placeholder in the text and format lists
page_text.append('table')
line_format.append('table')
# Check if we already extracted the tables from the page
if element.y0 >= lower_side and element.y1 <= upper_side:
pass
elif not isinstance(page_elements[i+1][1], LTRect):
table_extraction_flag = False
first_element = True
table_num+=1
# Create the key of the dictionary
dctkey = 'Page_'+str(pagenum)
# Add the list of list as the value of the page key
text_per_page[dctkey]= [page_text, line_format, text_from_images,text_from_tables, page_content]
# Closing the pdf file object
pdfFileObj.close()
# Deleting the additional files created
os.remove('cropped_image.pdf')
os.remove('PDF_image.png')
# Display the content of the page
result = ''.join(text_per_page['Page_0'][4])
print(result) |