Adventures in Machine Learning

Mastering OCR: How to Use Python for Machine-Readable Text Conversion

Optical Character Recognition (OCR) is a technology that allows us to convert images and other types of data into machine-readable text. Python is an excellent tool for OCR as it has many libraries and packages that make it easy to implement OCR in your projects.

In this article, we will explore how to use Python for OCR.

Beginning Steps

Before we get started, we need to ensure that we have all the necessary dependencies. If you are using Ubuntu or OSX, you can easily install Tesseract, Leptonica, and ImageMagick by using the following command:

“`$ sudo apt-get install tesseract-ocr libtesseract-dev liblept5 imagemagick“`

Downloading Dependencies

If you are using other operating systems, you can download the necessary packages from the official websites:

– Tesseract:

– Leptonica:

– ImageMagick:

Building Leptonica and Tesseract

Once we have all our dependencies installed, we can start building Leptonica and Tesseract. We can build Leptonica by using the following commands:

“`$ wget“`

“`$ tar xvfz leptonica-1.74.4.tar.gz“`

“`$ cd leptonica-1.74.4“`

“`$ ./autobuild“`

“`$ ./configure“`

“`$ make“`

“`$ sudo make install“`

We can now build Tesseract by installing the pytesseract package:

“`$ pip install pytesseract“`

Web-server Time!

We can now create our own OCR engine using Python.

We need to define a process_image() function that will convert an image into text. First, we can start by sharpening the image using ImageMagick.

We can then use pytesseract to extract the text from the image. def process_image(image_path):

sharpened_path = ‘sharpened.jpeg’

os.system(‘convert {} -sharpen 0x1 {}’.format(image_path, sharpened_path))

text = pytesseract.image_to_string(

return text

Optional: Building a CLI Tool for Your New OCR Engine

If you want to build a command-line interface (CLI) for your OCR engine, you can use the argparse module to create a CLI for your script.

This will allow users to input a filename and retrieve the text output.

Back to the Server

We can now integrate our OCR engine into a web server using Flask. We need to define a route handler and view function in our file to accept POST requests with Image data.

Inside this function, we can extract the text from the image and return it as a JSON response. @app.route(‘/ocr’, methods=[‘POST’])

def ocr():

file = request.files[‘image’]

img =

text = process_image(img)

response = {‘text’: text}

return jsonify(response)


We can create a simple front-end for our OCR engine using HTML, CSS, and JavaScript. We can use Ajax to upload the image to the server without reloading the page, and jQuery to handle the response and display the text output.

Conclusion and Next Steps

Using Python for OCR is a powerful tool that allows us to convert images into machine-readable text. We have explored how to download the necessary dependencies, build Leptonica and Tesseract, create our OCR engine, integrate it into a web server, and create a simple front-end.

We encourage you to explore the OCR technology further and see how you can use it to improve your projects. If you found this article helpful, don’t forget to star our repository on GitHub and hack away!

In this article, we explored the process of utilizing Python for OCR.

We discussed the importance of OCR and highlighted the reasons why Python is an excellent tool to deploy this technology. We discussed the necessary dependencies required to build Leptonica and Tesseract, and how to create an OCR engine and integrate it into a web server.

By following these simple steps, developers can extract machine-readable text from images and carry out meaningful actions with it. OCR can increase the efficiency and accuracy of data processing, and the power of Python makes this technology both accessible and easy to implement.

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