Program to Extract Text from PDF in Python

Extracting text from PDF reports is a typical prerequisite in different fields like information science, scholarly exploration, and business knowledge. This guide will investigate various techniques for extricating text from PDF documents utilizing Python, giving a nitty gritty gander at libraries like PyPDF2, pdfminer.six, and PyMuPDF. We will dig into establishment, essential use, and high-level elements of these libraries.

Introduction to PDF and Text Extraction

PDF (Portable Document Format) is a universal document design created by Adobe in 1993 to install reports in a way free of utilization programming, equipment, and working frameworks. Each PDF record typifies a total portrayal of a fixed-design level report, including the text, textual styles, illustrations, and other data expected to show it. This trademark makes PDFs exceptionally appropriate for sharing and saving the uprightness of records across different stages and gadgets.

Tools for PDF Text Extraction

To address these difficulties, a few libraries have been created in Python, each with its own assets and use cases. Among the most famous are PyPDF2, pdfminer.six, and PyMuPDF. These libraries give the usefulness expected to separate text from PDFs, however they contrast essentially in their methodology and capacities.

PyPDF2:

PyPDF2 is a pure Python library that gives functionalities to perusing and controlling PDF documents. It is generally easy to utilize and appropriate for fundamental PDF activities like consolidating, parting, and extricating text. Key elements of PyPDF2 incorporate perusing PDF records, separating metadata, and dealing with encryption and decoding. Its essential assets are its usability and inclusion of major PDF tasks. In any case, it needs progressed text extraction capacities and may battle with complex designs or non-standard text styles, making it less reasonable for point-by-point text extraction assignments.

pdfminer.six:

pdfminer.six is a more impressive and adaptable library devoted to separating text and data from PDF records. It offers definite command over the text extraction process, making it ideal for dealing with complex designs and different text styles. Its key elements incorporate hearty text extraction, design examination, and character-level control. The qualities of pdfminer.six lie in its capacity to perform point by point text extraction and design examination, making it reasonable for complex PDF records. Be that as it may, its high-level elements accompany expanded intricacy, making it more trying for novices. Also, its thorough parsing cycle can prompt more slow execution, particularly with enormous reports.

PyMuPDF:

PyMuPDF, also called fitz, is a quick and lightweight library for working with PDF records. It consolidates speed with cutting edge highlights, making it adaptable for text extraction and PDF control. PyMuPDF's key highlights incorporate fast text extraction, taking care of mind-boggling reports, and extricating pictures. Its assets incorporate elite execution and the capacity to deal with cutting edge text and picture extraction assignments, alongside report control abilities.

Python Libraries for PDF Text Extraction

A few Python libraries can assist with extracting text from PDF records. This guide will zero in on three famous libraries: PyPDF2, pdfminer.six, and PyMuPDF.

PyPDF2

PyPDF2 is an unadulterated Python library that takes into consideration control and perusing of PDF documents. It is easy to utilize and install, pursuing it a decent decision for essential message extraction undertakings.

Installation

To install PyPDF2, you can utilize pip:

Basic usage

Here is an essential illustration of how to separate message from a PDF utilizing PyPDF2:

Output:

 
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Explanation:

Importing the Library

• import PyPDF2: Imports the PyPDF2 library.

Defining the Function

• def extract_text_from_pdf(pdf_path): Characterizes a capability to extricate text from a PDF.

Opening the PDF Document

• with open(pdf_path, 'rb') as document: Opens the PDF record in parallel read mode.

Initializing PdfReader

• reader = PyPDF2.PdfReader(file): Makes a PdfReader object to peruse the PDF.

Instating Text Compartment

• text = "": Instates a vacant string to store the removed text.

Looping Through Pages

• for page_num in range(len(reader.pages)): Circles through each page in the PDF.

Extricating Text from Pages

• page = reader.pages[page_num]: Recovers the ongoing page object.
• text += page.extract_text(): Concentrates and adds text from the ongoing page to the text string.

Returning Removed Text

• bring text back: Returns the gathered text subsequent to handling all pages.

Example Utilization

• pdf_path = 'file:///C:/Users/JACKSON%20MICHAEL%20FARADAY/OneDrive/Documents/inf.pdf': Indicates the way to the PDF document.
• extracted_text = extract_text_from_pdf(pdf_path): Concentrates text from the PDF.
• print(extracted_text): Prints the extracted text to the control center.

pdfminer.six

pdfminer.six is a strong library for extricating text, pictures, and metadata from PDF records. It gives more command over the text extraction process compared with PyPDF2.

Installation

To install pdfminer.six, you can utilize pip:

Basic usage

This is an illustration of the way to remove text utilizing pdfminer.six:

Output:

 
This website is developed to help students on various technologies such as Artificial Intelligence, Machine Learning, C, C++, Python, Java, PHP, HTML, CSS, JavaScript, jQuery, ReactJS, Node.js, AngularJS, Bootstrap, XML, SQL, PL/SQL, MySQL etc.
This website provides tutorials with examples, code snippets, and practical insights, making it suitable for both beginners and experienced developers.
There are also many interview questions which will help students to get placed in the companies.   

Explanation:

Importing the Library

• from pdfminer.high_level import extract_text: Imports the extract_text capability from the pdfminer.high_level module.

Defining the Function

• def extract_text_from_pdf(pdf_path): Characterizes a capability named extract_text_from_pdf that takes one contention, pdf_path.

Extricating Text from PDF

• return extract_text(pdf_path): Utilizations the extract_text capability to extricate text from the PDF record at the predefined pdf_path and returns the removed text.

Example Usage

• pdf_path = ' file:///C:/Users/JACKSON%20MICHAEL%20FARADAY/OneDrive/Documents/inf.pdf': Determines the way to the PDF record.
• extracted_text = extract_text_from_pdf(pdf_path): Calls the extract_text_from_pdf capability with the pdf_path and stores the removed text in the variable extracted_text.
• print(extracted_text): Prints the extracted text to the control center.

PyMuPDF (fitz)

PyMuPDF, otherwise called fitz, is a lightweight PDF library that gives quick and productive text extraction capacities. It can deal with complex PDFs with pictures, tables, and different textual style styles.

Installation

To install PyMuPDF, you can utilize pip:

Basic usage

This is an illustration of the way to extricate text utilizing PyMuPDF:

Output:

 
This website is developed to help students on various technologies such as Artificial Intelligence, Machine Learning, C, C++, Python, Java, PHP, HTML, CSS, JavaScript, jQuery, ReactJS, Node.js, AngularJS, Bootstrap, XML, SQL, PL/SQL, MySQL etc.
This website provides tutorials with examples, code snippets, and practical insights, making it suitable for both beginners and experienced developers.
There are also many interview questions which will help students to get placed in the companies.   

Explanation:

Importing the Library

• import fitz: Imports the fitz module, which is the PyMuPDF library for taking care of PDF records.

Characterizing the Capability

• def extract_text_from_pdf(pdf_path): Characterizes a capability named extract_text_from_pdf that takes one contention, pdf_path.

Opening the PDF Archive

• report = fitz.open(pdf_path): Opens the PDF record at the predetermined pdf_path and makes a Report object named report.

Instating Text Holder

• text = "": Instates an unfilled string to aggregate the extricated text.

Circling Through Pages

• for page_num in range(len(document)): Circles through each page in the PDF record.

Stacking and Extricating Text from Each Page

• page = document.load_page(page_num): Burdens the ongoing page object by its page number.
• text += page.get_text(): Concentrates the text from the ongoing page utilizing the get_text() strategy and attaches it to the text string.

Returning Extracted Text

• bring text back: Returns the amassed text in the wake of handling every one of the pages.

Example Usage

• pdf_path = ' file:///C:/Users/JACKSON%20MICHAEL%20FARADAY/OneDrive/Documents/inf.pdf': Determines the way to the PDF document (' file:///C:/Users/JACKSON%20MICHAEL%20FARADAY/OneDrive/Documents/inf.pdf').
• extracted_text = extract_text_from_pdf(pdf_path): Calls the extract_text_from_pdf capability with the pdf_path and stores the extricated text in the variable extracted_text.
• print(extracted_text): Prints the removed text to the control center.