Optical character recognition (or in the abbreviated form: OCR) is a technology that helps computers, AI-powered algorithms, and other devices recognize text. Traditionally, OCR works based on patterns and correlations that allow this system to distinguish words from other elements visible on the image. However, this technique is quite old-fashioned and lacks desired accuracy, especially when it comes to longer texts or ones that have to be read in motion. That is why more and more companies that use OCR opt for systems that use deep learning. In this article, we are going to take a closer look at text recognition using machine learning and related technologies.
The main function of OCR is to take images that have textual elements and attempt to recognize what is written on them. It is used to identify texts such as handwritten characters or textual elements in the environment (such as license plate numbers or street signs).
Traditional OCR methods work perfectly for everyday uses such as scanning documents, where the OCR algorithm is able to recognize words with very high accuracy. However, nowadays, there are multiple cases of the use of OCR that need more technological advancement. There are at least three such instances:
City authorities make use of OCR to automatically monitor whether cars are parked according to road regulations. Parking inspectors with the use of mobile devices are able to scan license plates of vehicles and check whether they have permission to park in a given place.
SCANNING DOCUMENTS WITH MOBILE DEVICES
There are various mobile applications that enable users to take pictures of documents and convert them to text. Since photos may have uneven image angles, poor lighting conditions, or text quality, this task can turn out to be too challenging for OCR scanners.
DIGITAL ASSET MANAGEMENT (DAM)
It is an application of software that helps companies to organize media assets that include images, videos, or animations. DAM’s key feature that is commonly used is the ability to search through these assets. For example, OCR makes it possible and adds tags to them, making the searching process even more user-friendly.
Convolutional Neural Networks (CNNs)
There are two steps that text recognition is composed of:
- Detection of text areas in the picture or individual text characters within these areas
- Identifying these characters.
This is where deep learning comes in handy and enables character identification within images. How does it exactly work?
When a human sees something, the brain automatically labels, predicts, and recognizes specific patterns. Machines work similarly using CNNs. The same way we recognize patterns through our sensory abilities, CNNs break images down into numbers.
Convolution can be defined as the combination of two functions that produce the third function. That is why a network that uses convolution connects with each other sets of information and pulls them together to create a highly accurate representation of the image. Images are described with big data so that the deep learning algorithms can predict what they show. Such prediction enables the computer to perform operations such as unlocking a phone with the face recognition function or suggesting friends tag on pictures uploaded on Facebook.
Recurrent Attention Model (RAM)
Another deep learning-based model that uses the concept of human sight is the Recurrent Attention Model (RAM). It is based on the theory that when humans see a new object, certain parts of the image catch more attention. The eye tends to focus on these glimpses and, at least in the first place, gets information primarily from them.
System crops image to different sizes around one center and creates glimpse vectors from each version of the image. These vectors are passed to a location network which predicts the next part of the image that should be focused on. Step by step, the model explores new parts of the image till information from all the glimpses is good enough to achieve a satisfying level of accuracy.
Even though traditional OCR is a useful technology itself, deep learning can significantly improve its performance. RAM and CNNs are two of many models which can find their application in OCR. The future of image recognition undoubtedly lies in similar technologies since artificial intelligence proved to be beneficial to all niches in which it is commonly applied.
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