Discover how to manipulate PDFs using Python. Gain hands-on experience with real-life scenarios and broaden your knowledge in handling and processing PDF files efficiently.

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mypdftoolbox.tar.gz

pdf_compare

pdf_did_metadata

pdf_xmp_metadata

pdf_compute_checksum

pdf_merger

pdf_pages_splitter

pdf_pages_rotator

pdf_pages_remover

pdf_pages_shuffler

pdf_pages_watermarker

pdf_convert2img

pdf_extract_tables

pdf_extract_images

pdf_extract_links

pdf_annotator

pdf_redactor

pdf_parser

pdf_convert2docx

pdf_convert2pptx

pdf_compress

pdf_secure

pdf_crack

pdf_create

pdf_sign

pdf_scan

pdf_comment

pdf_compare_files

pdf_attach

pdf_extract_attachments

pdf_embed_js

pdf_change_rights

This course will provide you with hands-on experience in PDF manipulation using the Python programming language. It integrates the most common real-life scenarios into its proceedings and supplies you with a framework of "how to do it". 

This course is addressed to Python programmers who seek to broaden their knowledge in the Python programming language. Moreover, it targets those who are eager to gain in-depth experience in handling and processing PDF files which constitute a large part of our day-to-day lives.

PDF Management in Python

 ## Introduction ##

PDF documents are mainly created in two different ways. They are either generated by an electronic source, known as a **native** PDF, or by scanning in paper documents, known as a **scanned** PDF.

_Native_ PDF documents contain an internal structure that can be read and interpreted, whereas _scanned_ PDFs consist of scanned images, meaning that their content cannot be searched or edited.

# Performing OCR on a scanned PDF ##

**Optical Character Recognition** (**OCR**) is an adaptive technology that turns printed or written text into an electronic character-based file using a visual recognition process.

For instance, to convert a scanned PDF to an editable format such as a Text or MS. Word document, an OCR software is needed to analyze the “image” of each character that has been scanned in, and match it to an electronic character-based file.

# Scope ##

Whether you are struggling to extract information from scanned PDF contracts, invoices, or purchase orders, this lesson will aid us in developing a PDF OCR tool using the Python programming language.

# Requirements ##

The following Python libraries are needed:

## Pytesseractocr ###
This library is an interface to the tesseract OCR engine which is used for text detection.

## OpenCV ###
The **Open Source Computer Vision** Library is an exhaustive open-source library for computer vision, machine learning, and image processing. It supports a large variety of programming languages like Python, C++, Java, etc.

## PyMuPDF ###
This library is a Python wrapper for MuPDF. MuPDF surpasses other similar products by its rendering capabilities and supreme processing speed.

## Pandas ###
This open-source library provides high-performance, intuitive data structures, and data analysis tools.

## Numpy ###
This is the core library for scientific computation in Python. It offers high-performance multidimensional array objects and tools for working with them.

## Filetype ###
An open-source python library allows concluding a file or a buffer type by checking its signature.

|Library|Version |
|:-| - |:-| - |
|Pytesseract|0.3.7|
|OpenCV|4.5.3.56| 
|PyMuPDF|1.18.19|
|Pandas|1.1.4|
|Numpy|1.21.2|
|Filetype|1.0.7|

# Code examination ##

Let us pass through the main functions of this utility:

The function `scan_image` scans the content of a PDF page after being converted to an image. It performs the following steps:

1. Maintain copies of the image passed as parameters (**Lines 22-23**).
2. Convert this image to a binary format (**Line 26**).
3. Call the function `pytesseract.image_to_data` to launch the “tesseract OCR” engine, while specifying the binary format of the image as a parameter. This function returns an object of type dictionary containing the value, position, and confidence score of the text blocs detected (**Lines 35-36**).
4. Calculate the mean confidence score of all the text blocs grabbed from the image (**Line 39**).
5. Iterate throughout the grabbed text blocs while considering those having a confidence score greater than 30 for the sake of accuracy (**Lines 47-50**).
6. Draw a green bounding box around the text blocs if the parameter `highlight_readable_text` is set to True (**Lines 53-57**).
7. Search for the text specified as parameter `search_str` and for each matching value determine its positioning (**Line 61**).
8. Depending on the action chosen whether to `Highlight` or to `Redact`, draw a yellow or a black rectangle on top of the matching value (**Line 84**).
9. Save an image showing a comparison between the original screenshot of the PDF page and the processed version of the screenshot (**Line 102**).
10. Extract the text content out of the PDF page screenshot and save it to a "Pandas" data frame (**Line 108**).


 



Learn to manipulate scanned PDF documents using the Google Tesseract OCR engine. 

Manipulating Scanned PDF Files

Introduction

PDF Management Core Functions

Pages Processing

Content Processing

Document Processing

Conclusion

Appendices

Manipulating Scanned PDF Files

Introduction

Performing OCR on a scanned PDF