Python for image processing pdf

Extracting Text from Scanned PDF using Pytesseract & Open CV

Document Intelligence using Python and other open source libraries

The process of extracting information from a digital copy of invoice can be a tricky task. There are various tools that are available in the market that can be used to perform this task. However there are many factors due to which most of the people want to solve this problem using Open Source Libraries.

I came across a similar set of problem a few days back and wanted to share with you all the approach through which I solved this problem. The libraries that I used for developing this solution were pdf2image (for converting PDF to images), OpenCV (for Image pre-processing) and finally PyTesseract for OCR along with Python.

Converting PDF to Image

pdf2image is a python library which converts PDF to a sequence of PIL Image objects using pdftoppm library. The following command can be used for installing the pdf2image library using pip installation method.

Note: pdf2image uses Poppler which is a PDF rendering library based on the xpdf-3.0 code base and will not work without it. Please refer to the below resources for downloading and installation instructions for Poppler.

After installation, any pdf can be converted to images using the below code.

After converting the PDF to images, the next step is to highlight the regions of the images from which we have to extract the information.

Note: Before marking regions make sure that you have preprocessed the image for improving its quality (DPI ≥ 300, Skewness, Sharpness and Brightness should be adjusted, Thresholding etc.)

Marking Regions of Image for Information Extraction

Here in this step we will mark the regions of the image from where we have to extract the data. After marking those…

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README.md

Hands-On Image Processing with Python

This is the code repository for Hands-On Image Processing with Python, published by Packt.

Expert techniques for advanced image analysis and effective interpretation of image data

Image processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python.

This book covers the following exciting features:

• Perform basic data pre-processing tasks such as image denoising and spatial filtering in Python
• Implement Fast Fourier Transform (FFT) and Frequency domain filters (e.g., Weiner) in Python
• Do morphological image processing and segment images with different algorithms
• Learn techniques to extract features from images and match images
• Write Python code to implement supervised / unsupervised machine learning algorithms for image processing
• Use deep learning models for image classification, segmentation, object detection, transfer learning and neural style transfer

If you feel this book is for you, get your copy today!

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

viewer = viewer.ImageViewer(im) viewer.show() 

Following is what you need for this book: This book is for Computer Vision Engineers, and machine learning developers who are good with Python programming and want to explore details and complexities of image processing. No prior knowledge of the image processing techniques is expected.

With the following software and hardware list you can run all code files present in the book (Chapter 1-12).

Software and Hardware List

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

• Python Machine Learning — Second Edition [Packt][Amazon]
• TensorFlow Machine Learning Cookbook — Second Edition [Packt][Amazon]

Sandipan Dey is a data scientist with a wide range of interests, covering topics such as machine learning, deep learning, image processing, and computer vision. He has worked in numerous data science fields, working with recommender systems, predictive models for the events industry, sensor localization models, sentiment analysis, and device prognostics. He earned his master’s degree in computer science from the University of Maryland, Baltimore County, and has published in a few IEEE Data Mining conferences and journals. He has earned certifications from 100+ MOOCs on data science, machine learning, deep learning, image processing, and related courses/specializations. He is a regular blogger on his blog sites (https://sandipanweb.wordpress.com/, https://sandipandey.wixsite.com/simplydatascience, https://www.datasciencecentral.com/profile/SandipanDey and https://sandipanumbc.tumblr.com/) and is a machine learning education enthusiast.

Click here if you have any feedback or suggestions.

If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.

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README.md

Python Image Processing Cookbook

This is the code repository for Python Image Processing Cookbook, published by Packt.

Over 60 recipes to help you perform complex image processing and computer vision tasks with ease

Advancements in wireless devices and mobile technology have enabled the acquisition of a tremendous amount of graphics, pictures, and videos. Through cutting edge recipes, this book provides coverage on tools, algorithms, and analysis for image processing. This book provides solutions addressing the challenges and complex tasks of image processing.

This book covers the following exciting features:

• Implement supervised and unsupervised machine learning algorithms for image processing
• Use deep neural network models for advanced image processing tasks
• Perform image classification, object detection, and face recognition
• Apply image segmentation and registration techniques on medical images to assist doctors
• Use classical image processing and deep learning methods for image restoration
• Implement text detection in images using Tesseract, the optical character recognition (OCR) engine
• Understand image enhancement techniques such as gradient blending

If you feel this book is for you, get your copy today!

Instructions and Navigations

All of the code is organized into folders. For example,

The code will look like the following:

def get_grid_coordinates(points): xmin, xmax = np.min(points[:, 0]), np.max(points[:, 0]) + 1 ymin, ymax = np.min(points[:, 1]), np.max(points[:, 1]) + 1 return np.asarray([(x, y) for y in range(ymin, ymax) for x in range(xmin, xmax)], np.uint32) 

Following is what you need for this book: This book is for image processing engineers, computer vision engineers, software developers, machine learning engineers, or anyone who wants to become well-versed with image processing techniques and methods using a recipe-based approach. Although no image processing knowledge is expected, prior Python coding experience is necessary to understand key concepts covered in the book.

With the following software and hardware list you can run all code files present in the book.

Software and Hardware List

Chapter	Software required	OS required
1 — 9	Python 3.7, Anaconda version 2019.10 (py37_0), GPU (if available)	Windows, Mac OS X, and Linux (Any)

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

Sandipan Dey is a data scientist with a wide range of interests, covering topics such as machine learning, deep learning, image processing, and computer vision. He has worked in numerous data science fields, working with recommender systems, predictive models for the events industry, sensor localization models, sentiment analysis, and device prognostics. He earned his master’s degree in computer science from the University of Maryland, Baltimore County, and has published in a few IEEE Data Mining conferences and journals. He has earned certifications from 100+ MOOCs on data science, machine learning, deep learning, image processing, and related courses. He is a regular blogger (sandipanweb) and is a machine learning education enthusiast.

Click here if you have any feedback or suggestions.

If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.

Источник

Hands-On Image Processing with Python

Image processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python.

The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing.

• Perform basic data pre-processing tasks such as image denoising and spatial filtering in Python
• Implement Fast Fourier Transform (FFT) and Frequency domain filters (e.g., Weiner) in Python
• Do morphological image processing and segment images with different algorithms
• Learn techniques to extract features from images and match images
• Write Python code to implement supervised / unsupervised machine learning algorithms for image processing
• Use deep learning models for image classification, segmentation, object detection and style transfer

By the end of this Hands-On Image Processing with Python book, we will have learned to implement various algorithms for efficient image processing.

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