PDF Processing with Python. The way to extract text from your pdf... | by Ahmed Khemiri | Towards Data Science

Being a high-level, interpreted language with a relatively easy syntax, Python is perfect even for those who don’t have prior programming experience. Popular Python libraries are well integrated and provide the solution to handle unstructured data sources like Pdf and could be used to make it more sensible and useful.
PDF is one of the most important and widely used digital media. used to present and exchange documents. PDFs contain useful information, links and buttons, form fields, audio, video, and business logic.
As you know PDF processing comes under text analytics.
Most of the Text Analytics Library or frameworks are designed in Python only . This gives a leverage on text analytics. One more thing you can never process a pdf directly in exising frameworks of Machine Learning or Natural Language Processing. Unless they are proving explicit interface for this, we have to convert pdf to text first.
As a Data Scientist , You may not stick to data format.
PDFs is good source of data, most of the organization release their data in PDFs only.
As AI is growing, we need more data for prediction and classification; hence, ignoring PDFs as data source for you could be a blunder. Actually PDF processing is little difficult but we can leverage the below API for making it easier.
In this section, we will discover the Top Python PDF Library:
PDFMiner
PDFMiner is a tool for extracting information from PDF documents. Unlike other PDF-related tools, it focuses entirely on getting and analyzing text data. PDFMiner allows one to obtain the exact location of text in a page, as well as other information such as fonts or lines. It includes a PDF converter that can transform PDF files into other text formats (such as HTML). It has an extensible PDF parser that can be used for other purposes than text analysis.
PyPDF2
PyPDF2 is a pure-python PDF library capable of splitting, merging together, cropping, and transforming the pages of PDF files. It can also add custom data, viewing options, and passwords to PDF files. It can retrieve text and metadata from PDFs as well as merge entire files together.
pdfrw
pdfrw is a Python library and utility that reads and writes PDF files:
Version 0.4 is tested and works on Python 2.6, 2.7, 3.3, 3.4, 3.5, and 3.6
Operations include subsetting, merging, rotating, modifying metadata, etc.
The fastest pure Python PDF parser available
Has been used for years by a printer in pre-press production
Can be used with rst2pdf to faithfully reproduce vector images
Can be used either standalone, or in conjunction with reportlab to reuse existing PDFs in new ones
Permissively licensed
Slate
Slate is a Python package that simplifies the process of extracting text from PDF files. It depends on the PDFMiner package.
Step 1: Select Version of Python to Install from Python.org .
Step 2: Download Python Executable Installer.
Step 3: Run Executable Installer.
Step 4: Verify Python Was Installed On Windows.
Step 5: Verify Pip Was Installed.
Step 6: Add Python Path to Environment Variables (Optional).
Step 7: Install Python extension for your IDE.
I am working with Python 3.7 in visual studio code. For more information about how to setup your environment and select your python interepter to start coding with VS Code, check Getting Started with Python in VS Code documentation.
Step 7: Now you’ll be able to execute python scripts with your IDE.
Step 8 : Install pdfminer.six
pip install pdfminer.six
Step 9 : Install PyPDF2
pip install PyPDF2
Done! Now, you can start processing pdf documents with python.
PDFs Text Extraction Solution is based on three principal steps:
Merging Multiple and Large PDF Documents into a single Pdf Document.
Splitting Merged PDF Document into a set of documents (page by page splitting )
Handle splitted documents and Extract Text.
Full version of the proposed solution released on Github.
Kindly check it out via:
Forking and Starring repository is the best way to support the project.
github.com
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You can follow me on Github: https://github.com/ahmedkhemiri95