In this Python OCR crash course, we will learn how easy it is to get started with OCR and Python, the world’s most popular programming language.
Python OCR: A Crash Course
OCR is short for optical character recognition, an AI technique designed to extract written characters from images.
This AI technique, used in conjunction with other natural language processing techniques, can be used to create innovative software and app features. It is commonly used for workplace digitization and automation tasks such as the digitization of business records, though OCR can be used to automate other business functions that require the reading of written text, such as:
- Digitizing IDs
- Digitizing receipts
- Digitizing invoices
- Digitizing books
Python is one of the best languages to use for NLP, for several reasons:
- Python is a very user-friendly language
- Its global support from a passionate community of developers makes it one of the best languages to study for AI applications such as OCR
- There are libraries and tools for OCR development, as well as a wide range of other resources for NLP, AI, machine learning, data science, and more
Below will look at a few of the resources and technologies available for those interested in using Python for OCR.
Python Tesseract: An Open-Source OCR Engine
Tesseract, as the title of this section suggests, is Python’s open-source OCR engine, a wrapper for Google’s Tesseract-OCR engine. It is the best starting place for anyone interested in using Python for OCR.
With the right support, Python Tesseract can recognize over 100 languages. It can also be trained to recognize those that aren’t already supported.
Anyone familiar with Python will have no trouble getting started with this engine.
It is installed the same way other packages are, through commands such as “pip install” or “brew install.”
The primary function you will be using, image_to_data, includes easy-to-understand parameters that allow you to:
- Pass an image into the Tesseract engine
- Define the language to look for
- Customize the output for pandas
- Define the output type
Prerequisites are basic. You need Python 3.6 or above, the Python Imaging Library, and Google Tesseract OCR.
Although Tesseract is the main engine used, for OCR, it is important to use another engine for preprocessing and other tasks.
Using Tesseract with OpenCV
Using OpenCV, you can perform tasks essential to generating accurate OCR results, such as image preprocessing.
As with Tesseract, it is easy to use – simply install it using “pip install” or “brew install,” require it in your Python program, and begin using its functions.
Here are a few functions of this library:
- Read and write images
- Resize images
- Set a region of interest within an image
- Access and modify pixel values and image properties
- Rotate images
- Drawing basic geometric shapes within files
- Detecting features within images, such as edges and blobs (binary large objects)
- Perform a range of other manipulations, such as blurring and smoothing
In short, OpenCV can perform a number of tasks that can be used to preprocess images and enhance your ability to accurately extract characters from those images.
NumPy is one of the most widely used data science libraries in Python.
The more complex your OCR programs get, the more critical it will be to perform data-heavy manipulations on your images.
This means you will need to use a library designed for such tasks – in this case, NumPy.
One of the biggest reasons to use it is because it offers a number of features designed for multi-dimensional arrays.
When using it with the two packages mentioned above: namely, install NumPy as you would any other package and require it in your files.
Additional functionalities of NumPy include:
- Creating arrays
- Defining attributes for those arrays
- Arrange the elements within an array
- Performing mathematical calculations on those arrays
- Applying conditions to calculations on arrays
- Plotting arrays graphically
When working with OpenCV, NumPy can be used to perform tasks such as defining and applying different filters to images and performing more complex manipulations to images.
NumPy can also assist with other complex operations such as manipulating videos.
Python OCR is perhaps the best, easiest way to get started with OCR, for the reasons mentioned above. Not only is the language easy to learn, it contains libraries and features that are robust, widely supported, and professional-grade.
That being said, Python isn’t the only language that offers OCR, NLP, or AI support. In some cases, other languages may be more suitable since Python is not always the fastest.
Those interested in taking their career to the next level, for instance, may want to investigate other programming languages.
C++, for instance, is a fast programming language, which makes it useful to know for those interested in developing industrial-grade OCR applications.
Java, likewise, is a lower-level language than Python and it includes a number of native image recognition libraries, making it easy to develop OCR apps from scratch.When evaluating these options, start by assessing your own needs and the advantages offered by the various options, then choose the one that is most suitable for your use case. In some cases, Python will be the best choice, in other cases, a different language may be best, or, in others, you may find that no-code automation tools are best.