OCR (AKA Optical Character Recognition) is a technology that has rapidly revolutionized many walks of life. It helps you extract text from images and scanned documents to make content editable, updatable, and storable.
The advantage you ask? Easy digitization of books, newsletters, receipts, invoices, handwritten notes, and much more. Companies can easily populate their databases with the newly extracted information to make data in a structured form.
But, with that being said, how does OCR work? And, how can you leverage this technology to extract text from images?
Find two ways to do OCR in this article: Using Python vs. online tools. We will cover the caveats involved with both methods and recommend which you should use for your daily tasks. So, let’s get started.
Python is a very popular programming language that many machine-learning experts, AI specialists, scientists, and researchers use to solve real-life problems.
One such problem was to digitize handwritten or scanned documents and files so that we could store what was in the image on our digital devices.
Then came Tesseract OCR, which is a very popular Python library for extracting text from images, and much more.
Tesseract was initially developed as a research project by HP in 1995 and open-sourced in 2006 by Google. Today, we see the Tesseract engine as a powerful source of doing OCR in Python language.
The Tesseract engine comes with built-in support for 100+ languages. So, documents of any language and type can be accessed with the Pytesseract library for conversion to digital text. Plus, you can get output in PDF and plain text files, further boosting productivity.
Tesseract pre-processes the input image first to improve its quality and remove blurriness or noise. After that, it examines the page’s arrangement/orientation to determine text blocks, paragraphs, and characters.
With the help of machine learning and conventional image processing approaches, the OCR engine matches patterns in the segmented areas and recognizes individual characters.
Lastly, post-processing operations like spell-checking and error correction are used to ensure that the extracted text is pristine. Finally, the user gets the digitized text in whatever format they desire.
To leverage the Tesseract engine for OCR, you first need to install two major libraries in a suitable IDE.
“pip install pillow pytesseract”
After this installation, you’ll need to install the Tesseract .exe file from this link. This will ensure that you can access the engine from the command line.
The next step is to select a suitable image for the process. It should steer clear of any horizontal or vertical deviations and keep things simple (for now.) Below is the screenshot of the image that we used.
Now is a good time to write the Python code that you’ll execute to extract text from the image. We’ll use the given code to fetch the image file and get it converted to a text file.
For the sake of simplicity, we haven’t performed any binarization, or noise removal steps as our sample image was clear enough.
However, there are a lot more enhanced image-processing methods that you can play with using the Pillow library. You can also batch-process images, change the input and output languages, and more using the base Python code.
Anyhow, below is the text file that we received for running our code in the command prompt line.
Considering all the pros and cons, we recommend using Python OCR only for developers or advanced users needing privacy and customization in the process.
Today, we have many online tools that can quickly and accurately extract text from images without any hassle. These include the tools like:
All these tools have one thing in common: a combination of Tesseract OCR engine along with latest Machine Learning algorithms to improve text extraction accuracy.
That’s right, the online OCR tools are built upon the very Tesseract Python library we discussed before. The only difference is that these tools are already trained very rigorously on hundreds and thousands of parameters.
So, their results are often very accurate and they can process a wide range of images and languages without causing any problems.
Just like the Tesseract OCR process, the tools start with Pre-processing setting the image up for the process. Then, they segment areas and recognize characters individually. Finally, they run the post-processing algorithms to ensure the accuracy of extracted data.
One advantage that these online tools have over Python OCR is their installation of the latest ML algorithms.
The training at the backend helps the tools pin down mathematical text, different kinds of handwriting, and much more. You can achieve this precision with Python OCR, too, but that will take a lot of coding and hassle.
To start the process, we will randomly choose the online tool on the list, however, you’re free to choose any option you like.
Next, it is time to upload the same image we used with the Python OCR process. This will give us a good comparison line between the two methods.
To upload, you can either drag and drop the file into the tool’s interface or browse it through your computer. There’s also an ‘URL’ option to grab an image from any source on the internet.
Afterward, simply click the ‘Extract’ button to begin the text extraction process. After a few moments, you’ll get the results like this:
Here, we can either copy the result to the clipboard or download a text file to see the output. If you choose to save the .txt file to your device, then here’s how it will look:
Based on all the pros and cons we just shared for the online OCR tools, we recommend it for a wide range of users who need quick solutions.
The reason is that you don’t have to set up elaborate codes or environments to make the tools work. So, they are convenient for a variety of tasks and can be your daily drivers for work.
OCR technology has drastically transformed various industries by enabling the extraction of text from images and scanned documents. Python OCR offers customizable parameters and no processing limits, making it ideal for developers or advanced users.
On the other hand, online OCR tools like imagetotext.info provide user-friendly interfaces, quick processing, cross-platform accessibility, and APIs for pre-trained OCR models.
Despite limitations such as internet dependence, online tools are recommended for users needing swift and hassle-free text extraction solutions without the need for complex setups.