Extracting Text from Scanned PDF using Pytesseract & Open CV

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.

pip install pdf2image

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.

https://anaconda.org/conda-forge/poppler

https://stackoverflow.com/questions/18381713/how-to-install-poppler-on-windows

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

from pdf2image import convert_from_path

	pdfs = r"provide path to pdf file"
	pages = convert_from_path(pdfs, 350)

	i = 1
	for page in pages:
	    image_name = "Page_" + str(i) + ".jpg"  
	    page.save(image_name, "JPEG")
	    i = i+1        

Convert PDF to Image using Python

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.)

#pytesseract #python #cv2 #ocr #invoice

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Introduction to Structured Query Language SQL pdf

SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.

Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:

1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.

2. Every database seller needs an approach to separate its item from others.

Right now, contrasts are noted where fitting.

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How to Extract Text From PDF Files in All Formats.

Do you need to extract text from different files such as pdfs and Word files?

This quick tutorial shows how sort files by type, and then extract text from PDF files. I downloaded two fake resumes in pdf format from Overleaf to demonstrate how this code works. I am not going to cover how to extract text from Word documents. You can download docxpy Python package and use it to extract text from Word files. Feel free to contact me at anna@sakura-ai.com if you have any questions or need help parsing documents.

The main challenge in extracting text from PDF files is that they have different formats:

• PDF files are either 8-bit binary files or 7-bit ASCII text files (using ASCII-85 encoding).

• Every line in a PDF can contain up to 255 characters.

• Every line ends with a carriage return, a line feed, or a carriage return followed by a line feed (depending upon the application or platform used to create the PDF file).

• PDF is case sensitive.

• The file format is completely independent of the platform that it is viewed or created on. Files can be moved back and forth between Macs, Windows system, Linux systems,… When FTP-ing a PDF file, it does make sense to compress it, to avoid data corruption by some outdated web system that the file needs to go through.

• Scanned PDFs are stored as images

#text-extraction #python3 #pdf-text-extractor #pdf

Extracting Text from Scanned PDF using Pytesseract & Open CV

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.

pip install pdf2image

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.

https://anaconda.org/conda-forge/poppler

https://stackoverflow.com/questions/18381713/how-to-install-poppler-on-windows

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

from pdf2image import convert_from_path

	pdfs = r"provide path to pdf file"
	pages = convert_from_path(pdfs, 350)

	i = 1
	for page in pages:
	    image_name = "Page_" + str(i) + ".jpg"  
	    page.save(image_name, "JPEG")
	    i = i+1        

Convert PDF to Image using Python

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.)

#pytesseract #python #cv2 #ocr #invoice

Extracting Text from Scanned PDF using Pytesseract & Open CV

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.

pip install pdf2image

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.

https://anaconda.org/conda-forge/poppler

https://stackoverflow.com/questions/18381713/how-to-install-poppler-on-windows

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

from pdf2image import convert_from_path

	pdfs = r"provide path to pdf file"
	pages = convert_from_path(pdfs, 350)

	i = 1
	for page in pages:
	    image_name = "Page_" + str(i) + ".jpg"  
	    page.save(image_name, "JPEG")
	    i = i+1        

Convert PDF to Image using Python

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.)

#pytesseract #python #cv2 #invoice

How to extract tables from PDF using Python Pandas and tabula-py

A quick and ready script to extract repetitive tables from PDF

This tutorial is an improvement of my previous post, where I extracted multiple tables without Python pandas. In this tutorial, I will use the same PDF file, as that used in my previous post, with the difference that I manipulate the extracted tables with Python pandas.

The code of this tutorial can be downloaded from my Github repository.

Almost all the pages of the analysed PDF file have the following structure:

Image by Author

In the top-right part of the page, there is the name of the Italian region, while in the bottom-right part of the page there is a table.

Image by Author

I want to extract both the region names and the tables for all the pages. I need to extract the bounding box for both the tables. The full procedure to measure margins is illustrated in my previous post, section Define margins.

This script implements the following steps:

• define the bounding box, which is represented through a list with the following shape: [top,left,bottom,width]. Data within the bounding box are expressed in cm. They must be converted to PDF points, since tabula-py requires them in this format. We set the conversion factor fc = 28.28.
• extract data using the read_pdf() function
• save data to a pandas dataframe.

In this example, we scan the pdf twice: firstly to extract the regions names, secondly, to extract tables. Thus we need to define two bounding boxes.

#data-collection #tabula-py #data-science #pdf-extraction #python #how to extract tables from pdf using python pandas and tabula-py