What is Optical Character Recognition (OCR): Overview and use cases
We’ve all been there — standing in the grocery store holding a product that’s covered in a foreign language, waiting for our smartphone camera to scan the text and give us a translation of it to know exactly what we’re looking at. Similarly, when you receive a PDF document and can’t copy any of the text so you opt for converting it to a different file type instead. Did you know that all of this is possible thanks to optical character recognition (OCR)? Let’s dive into the basics of what OCR is, how it works, the problems it solves, and why it’s an integral part of modern technology for now and decades to come.
Today we’ll cover:
- What is Optical Character Recognition (OCR)?
- How OCR works
- Advantages & limitations of OCR
- Use cases and applications
- Key takeaways
What is Optical Character Recognition (OCR)?
When we say Optical Character Recognition, we refer to the process of extraction and conversion of handwritten or typed text from image, video, or scanned documents like PDF to a digitally modifiable format. It is a field of study in artificial intelligence, more specifically tied to computer vision and pattern recognition. With OCR, we can encode printed text from an image, allowing it to be electronically altered, searched, stored more compactly, presented on the web, and utilized in machine processes like cognitive computing and more. From here, the information obtained from OCR may be applied to a vast array of uses that range from personal use to public security.
Typically, OCR is divided into two types — traditional OCR and Handwriting OCR. In essence, both aim to reach the same result but differ in the information they encode. Traditional OCR considers the extraction of text from common font styles that the OCR system can easily be trained with. Written handwriting, on the other hand, is unique to each individual, making it that much harder for even AI to encode. Letters and words can significantly differ in style than typed text, impacting readability. It’s often even difficult to read other people’s handwriting with our own two eyes, let alone expect AI to annotate each word with maximal accuracy. Despite its difficulty and limitations, Handwriting OCR technology nonetheless exists but requires persistent training for optimal pattern recognition.
How OCR works
Optical character recognition is primarily achieved with the combination of both hardware and software elements. There must be the presence of a device that scans the physical document and then the software, which converts it into digital code. PDF to text file conversion, on the other hand, eliminates the need for the additional scanner component. Let’s take a closer look at how the OCR system works for the former process.
Phase 1: Pre-processing
To begin, the hardware component, which can be any type of optical scanner, converts the document’s physical shape into a digital image. For example, if there’s a document on a piece of paper, the hardware component achieves rendering a digital copy of that exact same document. During this process, it’s important for the OCR technology to define the areas of interest in the image. In this case, the areas of interest are the ones that contain text, considering the empty sections as null. That process is otherwise referred to as converting the image into backgrounds (white, blank areas) and characters (evidently dark areas).
Phase 2: Character recognition
Once the backgrounds and characters have been separated, it is possible to initiate the process of identifying the specific contents of the characters. The dark areas, or characters, identify numbers and letters. The analysis of these characters is not done in bulk but in small segments at a time. Typically, that refers to a single word at a time if the AI recognizes the language and letters of the text accurately and it is clear to read on the text. The specific method for character recognition is carried out by pattern recognition or feature extraction, with the latter being implemented for the recognition of newer characters.
- Pattern recognition — The AI is trained with a variety of text samples in order to be able to autonomously find matches by comparing the text from scanned documents to the characters it has already learned.
- Feature extraction — The system can usea set of rules to detect specific characteristics of the letters or numbers. That includes but isn’t limited to the angles of the lines and their positions. For example, when we look at the letter “A” that’s all it is to us. However, for the AI it’s a combination of three lines, two of which are angled and one is horizontal.
Phase 3: Post-processing
After all of the characters in a given document are identified, they are then converted to an ASCII code that can be stored for further use. Unfortunately, no system is foolproof, not even the best ones in the world. That is why most OCR systems carry out a post-processing phase that will essentially double-check the initial output. For example, the characters ‘O’ and ‘0’ can be nearly indistinguishable, especially when handwriting is involved. That is what makes the post-process phase crucial for accuracy.
In brief, the OCR pipeline looks something like this:
Input image -> pre-processing -> text detection -> text recognition -> post-processing -> output text
Advantages & limitations of OCR
OCR holds a steady place in the modern world, but before we look at the specific applications and use cases, let’s identify the benefits and shortcomings of OCR technology.
The noteworthy advantages of OCR systems includes:
- Automates manual processes — OCR technology has essentially eliminated the need for manual data entry. As long as the text is comprehensible, it can easily be transferred from paper to digital format in record time compared to manual input.
- Cuts down on labor time — In addition to the previous point, think about how much time it took to scan or transfer data from physical to digital records. OCR systems carry out the same processes in a fraction of the time needed to do it manually.
- Opens avenues for innovation — As we’ll look further into OCR applications, we’ll see that OCR paved the path for enhanced technology and streamlined processes that could not be imagined decades ago. That includes but is not limited to implementing OCR technology for aiding people with physical impairments and vehicle tracing.
Here are also a couple of limitations that OCR technology proposes currently:
- Blur and movement — One of the considerable shortcomings of OCR is when the image is not still or there is blur detected, recognition accuracy decreases. This still requires further enhancements to perfect for future use.
- Room for improvement — OCR technology on its own carries out one specific function: recognizing and annotating text. That, in its turn, must be trained with datasets prior to use. For more complex applications and additional capabilities, OCR must be combined with machine learning or deep learning AI.
Use cases and applications
The more we look, the more we’ll see applications of OCR immersed in our daily lives from personal use to life-changing technological advancements. We’ll only highlight only a handful of the many fascinating use cases and applications of optical character recognition.
Preservation of documents
Antique books, historical documents, personal records, and much more vital information exist in the form of ink-on-paper records. Not everything came with an electronic version before it appeared on paper or it isn’t accessible currently. OCR technology offers us the immense opportunity to digitize such content, rendering it indestructible compared to its previous fragile form.
Banking and finances
Simply think of the mass of data that banks maintain and organize each moment from transactions to contracts, checks, loans, statements, and so on. Digitizing all of it streamlines processes significantly, allowing data to be stored with ease and fetched when necessary. And we can’t forget about how mobile banking apps make our lives easier. Without OCR, they wouldn’t be able to offer an array of features that we use daily.
Number-plate recognition
Automatic number-plate recognition (ANTR) is one of the OCR applications that has been prevalent for many years and will continue to be. ANTR technology is used in countless countries to ensure national security from road violations to as far as tracking criminal activity. The number-plates in their turn are electronically attached to the driver’s credentials, streamlining the process of owner identification. Since number plates are made up of a combination of a handful of numbers and letters with a clear font, it is significantly easy to track the number plates and with great accuracy, too.
Text-to-speech
For people with physical impairments such as blindness, OCR systems are a life-changing aspect of their daily lives. The most noteworthy example of that is text-to-speech technology that allows text to be scanned from both digital and physical contexts and then read aloud by the device. In fact, this is one of the earliest applications of OCR since there has always been a need to make certain actions more accessible to the visually impaired. Now, optical character recognition technology is more enhanced than ever before by expanding to support hundreds of languages and dialects.
Key takeaways
The extraction of textual data from scanned documents or images (PDF, TIFF, JPG) into model-understandable data is known as optical character recognition (OCR). OCR solutions are designed to make information more accessible to users. It enhances company operations and processes by reducing the amount of time and resources required to maintain unsearchable or difficult-to-find data. Businesses can use OCR-processed textual material more readily and rapidly once it has been transmitted. Its advantages include the removal of human data input, the decrease of errors, and increased productivity, among others.
Originally published at https://blog.superannotate.com.
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