Introduction:
Extracting text from OCR PDFs aids in reorganizing data, converting unstructured text into structured formats, creating reports, and analyzing content with NLP techniques, enhancing business processes and insights derived from digital documents. However, using Python to OCR PDF can be a bit complex.
summary

Native PDF text extraction relies on font and encoding data. OCR tools convert scanned PDF images into searchable text, enabling efficient data handling and analysis. This insightful blog post will help you explore practical techniques for running OCR on PDF and extracting your text without any problems.

What Do You Need to Install Before OCRing PDF in Python

Optical Character Recognition (OCR) technology empowers computers to extract text from images or scanned documents, streamlining data processing and analysis by eliminating manual transcription. Python offers several OCR libraries that facilitate text extraction from PDF files.

Python's PDF OCR libraries enable developers to convert PDF pages into images and subsequently extract text, leveraging Python's capabilities to automate data extraction tasks efficiently and effectively from large volumes of documents.

Before you get started to extract text from scanned PDFs, ensure you have the following installed on your system or device.

The necessary requirements for OCR PDF in Python include:

  1. ocrmypdf==12.2.0
  2. camelot-py==0.9.0
  3. Ghostscript

Additional requirements:

  1. Pillow==8.2.0
  2. pytesseract==0.3.7

How to OCR PDF in Python

Optical Character Recognition (OCR) is a crucial electronic tool for converting scanned images or handwritten text into editable computer files. Python supports various OCR libraries like Pytesseract, utilizing Google's Tesseract-OCR engine. However, using Python to OCR PDF is a complex process. 

Pytesseract can recognize text in PDFs across 100+ languages. Python's PDF OCR software versatility facilitates seamless integration with OCR libraries, enabling efficient autonomous handling of large datasets. Combined with machine learning techniques like NLP and object detection, Python empowers advanced computational capabilities.

Learn how to OCR PDF in Python using the Try and Except method.

Step 1: Import the necessary modules

Import `os` and `pytesseract` modules. Next, import the `Image` module from the `PIL` package. Use the `convert_from_path` function to convert PDF files to images for OCR processing with Python.

OCR PDF in Python

Step 2: The function takes a file name and initializes the empty list

The function takes one parameter (input file name) and initializes an empty list.

Step 3: Convert the text and generate a filename

In a try block, iterate through each image in the images list, converting it to text. Then, generate a filename for each image and save it as a JPEG.

Step 4: Extract text with pytesseract and handle exceptions

Extract text using pytesseract and append it to the list. Print exceptions encountered during these operations.

Step 5: Modify file extension to .txt format

Create output filename by removing input file extension and adding .txt extension.

OCR PDF in Python

Step 6: Assign a PDF file and return the filename

Assign input filename to `pdf_file`. Write extracted text to the output file with a modified extension. Next, return the output file name.

Step 7: Read and print the PDF output

Invoke `read_pdf` with `pdf_file,` and then print the output.

Extra Part: Run OCR on PDFs with Python Alternative

SwifDoo PDF is a versatile Windows editor that lets you OCR PDF in Python. It features an OCR engine that converts image-only PDFs into editable text. This makes it ideal for recognizing and manipulating text within scanned documents and images embedded in PDF files.

In addition, this reliable program allows you to annotate, edit PDFs, password-protect, and convert your documents with just one click.

Let's learn how to run OCR on PDF with the SwifDoo PDF program.

Step 1: Download and launch SwifDoo PDF from the official website or Microsoft AppSource for OCR capabilities.

Step 2: Launch the software and tap "OCR" in the Edit tab to begin.
OCR PDF in Python alternative

Step 3: In the Recognize Document window, select OCR options to convert PDFs to formats like text with images, original formatting, searchable non-editable text, or pure text.

Step 4: If necessary, specify page ranges when choosing the OCR output format. Click OK to initiate OCR processing of the PDF.

Note: To exclude specific areas from OCR, use the "+" button to select them. Text within these areas won't be recognized when processing the PDF for OCR.

Final Wrap Up

Python's OCR libraries, such as EasyOCR, Tesseract, and Amazon Textract, automate tasks, streamline workflows, and extract insights from unstructured data. These libraries evolve with advancements in machine learning and computer vision to enhance capabilities across industries.

We have provided two ways to use Python to OCR PDF files. While the Try and Except method is a great technique, many users may find it complex to use. In such a case, you can rely on the professional expertise and ease of the SwifDoo PDF. It is a versatile and reliable way to manage all your PDF queries.

FAQs

Q:Is Python good for OCR?

Python offers robust OCR libraries such as Tesseract and EasyOCR. These libraries use deep learning to extract text from images and documents. Tesseract provides accuracy, EasyOCR usability, and Amazon Textract efficiency. These tools automate tasks, analyze data, and leverage machine learning and computer vision advancements, driving OCR innovation and capabilities forward.

Here's how each library contributes when you run OCR on PDF.

  • EasyOCR: EasyOCR simplifies text extraction for accessibility and data entry automation.
  • Doctr: Doctr facilitates efficient document understanding, layout analysis, text extraction, and semantic comprehension.
  • Keras-OCR: Keras-OCR extracts text from images accurately and efficiently.
  • Tesseract: Integrate Tesseract OCR to extract text from photos in Python.
  • GOCR: GOCR extracts text from photographs across various platforms.
  • Pytesseract: Integrate Pytesseract for OCR text extraction from photos in Python.
  • OpenCV: OpenCV provides extensive image manipulation, object detection, motion tracking, and more.
  • Amazon Textract: Integrates with AWS for scalability, reliability, and secure document processing across sectors.

Q:What is the most accurate OCR in Python?

Finding the best Python to OCR PDF can be tricky. It offers powerful OCR libraries for text extraction from images and documents, automating tasks like data entry and analysis while streamlining workflows and extracting insights from unstructured data.

Tesseract is one of the best and most accurate Python PDF OCRs. Integrating Tesseract OCR in Python allows for accurate text extraction from photos.

Tesseract, maintained by Google, excels in deciphering text from various image types, including scanned documents and photos, and supports over 100 languages. Developers can customize parameters like page segmentation mode and language models to enhance accuracy based on specific use cases.

Integration with Python is straightforward, enabling seamless incorporation of OCR capabilities into applications.

Optimization strategies such as adjusting parameters and preparing images can further improve Tesseract's accuracy, accommodating varying text complexities and image qualities for robust OCR performance in diverse applications.

Lydia is an English columnist for AWZware. She is familiar with different technology products such as office software, location changers, and video software applications. Focused on tech blog writing, she is willing to solve your problems with up-to-date information.

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