Optical Character Recognition (OCR) is a technology that allows computers to scan and extract text from digitally un-editable sources such as handwritten documents, pictures, and signboards.
These sources are not limited to just non-digital sources, but also include digital pictures and non-editable file formats such as PDF.
The extracted text is in Unicode and can be modified using text editors and word processors.
OCR technology has many uses and advantages. In this article, we will see how it works, what are its use cases and why using OCR is advantageous for businesses.
How Does OCR Work?
OCR is an application of Artificial Intelligence (AI). More specifically, it is an application of machine learning.
Machine learning is the branch of AI that is concerned with pattern recognition and learning. Computers can be trained using machine learning to enable them to recognize patterns in unidentified data sets. This is the most important point in OCR technology.
Now, we will take a look at how the entire process of text extraction from digitally uneditable sources is done using OCR.
1. Scanning the Source
In OCR, the first step is scanning the source that contains the text. The source in question could be anything such as:
- Physical Documents
- PDF documents
Whatever the source may be, the first step is to scan it.
For non-digital sources, there needs to be a dedicated scanner or camera present. A physical document can be converted to a digital one by using a scanner, or its pictures can be taken using a camera.
Digital sources, on the other hand, can simply be uploaded or imported to the software.
Once the scanning part is done the program moves on to the next step.
2. Cleaning the Image
Cleaning is a term used by graphic designers which refers to removing noise from an image. Noise can be anything in the image that distorts its quality. In OCR, such noise can be due to dust particles that settled on the source, or some blurring that might have occurred due to the camera shaking.
Any blurriness is removed by sharpening the blurred parts and stray pixels. Here, particles are cleaned as well and any imperfect edges are flattened.
Once all these types of impurities have been removed from the image, the program performs the next step.
3. Converting the Image to Two Colors
In a process known as binarization, the cleaned images have their contrast maximized. This results in the picture becoming completely black and white, where black (or dark) parts of the image are text, while the white parts are the background.
This is a common technique used in a lot of machine learning applications such as sign language interpreters etc. In sign language interpreters, turning the background entirely black while keeping the hands white makes it easy to detect all the shapes the hands make. Similarly, any black text that is against a white background is easily identifiable.
The same thing happens with OCR.
The program needs to match the contours and edges of each word with ones in its database, hence they need to be easily detectable.
After this step comes the most important part of the entire process.
4. Character Recognition
The character recognition part is where machine learning shows its power. However, there are two ways in which it is applied. Each method has a different algorithm. The algorithms can be classified as follows:
- Pattern Recognition
- Feature Detection
Pattern recognition algorithm tries to match the characters and their font styles to those found in a database. The closest match possible is considered to be the character in question.
Feature detection algorithm tries to match the shapes and number of the contours and edges of each character to find the closest match possible.
A common drawback in both algorithms is that they are not 100% accurate. People can have very different handwriting. These algorithms sometimes cannot match the proper characters.
When the handwriting is in cursive, it makes things extra hard. Cursive writing adds contours to letters that aren’t usually present, this makes identifying them even harder.
Anyhow, an accuracy of 98% is considered acceptable. Any tool that provides such accuracy is good enough to be used in a real situation.
Once the text has been extracted, it is compiled into a file that can be edited using word processing software and text editors. The format it is compiled into depends on the software that is being used. Some tools only make Word files while others can save them in PDF, TXT, and EPUB as well.
So, choosing what kind of output file you want is something to be considered when choosing an OCR tool/application.
Advantages of OCR
OCR technology has brought many benefits, especially to businesses. The advantages are not always apparent because OCR itself is a hidden technology. We utilize it in our day-to-day lives without noticing it at all. A great example is the Google Assistant application. Its image search and image translation services both use OCR.
With that said, here are some advantages OCR has brought to the business world.
1. Automation of Document Flow
Before OCR was a commercial success, companies that had just started shifting to digital document storage found themselves with a huge backlog of physical copies that had to be ‘digitalized’. Manually copying all the documents to computers was obviously not an option.
But with OCR, these documents could be easily converted to digital ones and saved in a digital format. With just a dash of scripting required, the entire process could be almost entirely automated. Human intervention was only required at the first level of input where the physical documents were fed to a scanner.
2. Increase Productivity
Due to automated document flows, the time required to find and retrieve data is cut down drastically. Digital files are far easier to index and find compared to physical copies.
This reduces the time wasted in finding the correct documents and saves people trips to the records room.
3. Saves Money
By utilizing OCR, businesses can cut down on the costs required to hire a professional data extractor. Instead, OCR technology can just do the job for you at a fraction of the cost.
It also cuts down on storage space and eliminates the need for file cabinets, so you do not need to pay for those either. This frees up more office space for more productive activities.
4. Increases Accuracy and Efficiency
Humans can make mistakes. Whether they are intentional or not is beside the point. Machines, on the other hand, only make mistakes if they were programmed to do so or had a serious fault in their mechanism.
So, any task performed by a machine will have consistently high efficiency as compared to humans whose efficiency varies from day to day and even the time of day. With OCR, you can practically forget about inputting the wrong data accidentally since all of it is automated.
5. Better Data Security
Digital documents are at less risk of getting stolen or harmed compared to physical documents. You do not need any technical skill or years of study to break open a lock and steal some papers, but you do need it for hacking a computer or a server.
Couple that with advanced security measures computers are just loaded with, it is incredibly difficult for any run-of-the-mill hacker to be able to access and steal/sabotage digitally stored files.
6. Improves Customer Service
When any documents filled out by customers are stored digitally using OCR, they can later be retrieved extremely easily. This allows customer support employees to easily find out the details of a customer such as any request they might have submitted or some inquiry they had on the fly.
This allows them to save a ton of time as customers do not have to wait on phone calls for more than a few seconds before their requests are handled. This point also ties back into the increased efficiency point.
7. Crisis Management
Digital storage, due to OCR, has much better chances of surviving disasters such as fire or natural disasters. That’s because the data on a server is also backed up on other servers in completely different locations.
In the case of an accident, physical copies of documents would be lost forever, while digital data can still be recovered from the backup servers. This enables businesses to bounce back and start where they left rather than start from scratch.
Hence, businesses can continue without too much of a hitch. This is extremely important for large enterprises as even a delay of a single day can result in significant losses which is antithetical to the very idea of business.
OCR Use Cases
OCR and image-to-text conversion have plenty of real-world use cases. In this section, we are going to look at a few systems that use OCR.
1. Traffic Monitoring
In most of Europe and the USA, there are security systems in place to monitor traffic and enforce the traffic rules. Such systems are installed at places like traffic lights and toll plazas on highways.
The way they utilize OCR is through cameras that are set near the ground and can accurately read the number plates of all passing cars. This allows them to quickly snap a picture of the number plate if that car has broken a signal or if it had some kind of prior record.
These systems are linked to the police database and can grasp whenever a missing\stolen vehicle or a vehicle used in a crime has passed by. They can raise an alarm in the nearest police precinct so that they can apprehend the vehicle.
For normal folk, who have just broken a signal or ran a stop sign, their plates are noted and using the very same database which contains information on all registered cars, a fine is generated and mailed to the house of the offender.
2. Digitizing Old Documents
While OCR and image-to-text conversion have been around for a long time now, it was not that long ago that they were commercialized. Even now, large corporations have huge banks of physical documents. Governments also have huge storage banks for just physical documents.
OCR is used to efficiently make these documents digital and save them electronically, which saves up a lot of space and is more secure in the long run. We already discussed how OCR has the advantage of automating the document workflow, which directly applies in this use case as well.
In fact, even now workflows still require physical documents in quite a lot of places. Using an effective and accurate image to text converter can facilitate the process of moving those documents from the point of origin to all the relevant personnel.
3. Text-to-Speech Systems
Text-to-speech is a great application of image-to-text conversion. Text-to-speech works in the following way.
The system reads from an image or a file and then plays it out loud. Text-to-speech is great for visually impaired people. It allows them to read books, signs, and even text written in an image. With the power of OCR, these people can also enjoy these hobbies without being limited by their impairment.
Text-to-speech is also great for learning. Learning new languages has become quite common in the modern world. Due to the internet, the world has gotten “smaller” in that people from around the world can meet each other and chat or talk through the internet. Entire businesses run on the internet alone without even having a physical office.
So, learning languages is something that is lucrative. Students can use text-to-speech programs to learn how to pronounce something written in a different language. It can also be used in tandem with translation software to “speak” in a foreign language.
4. Partial Automation in Exams
Cambridge Assessments is a company that is responsible for holding the GCSE and IGCSE examinations all over the world.
GCSE and IGCSE are some of the biggest exams that middle school and high school students go through. In some countries, they are known as A ‘levels and O ’levels where “A” means advanced and “O” means ordinary.
The nature of the examination is such that solved question papers are collected from all over the world and then sent to Britain for checking. However, the people responsible for checking them are not necessarily in Britain.
With the rise of remote jobs and online working especially after Covid, many people have decided to permanently do remote jobs. Such workers are found everywhere and Cambridge Assessments is no exception.
Instead of wasting resources on logistics, they simply use an OCR system that scans each paper, makes a digital copy of it, sorts it according to the subject and other sorting conditions it may have, and then just sends the digital copy to the relevant checker.
5. Verifying Documents
Everybody is quite conscious of the security of their personal information nowadays. This is especially true in the case of companies with many clients. They take the security of their clients’ data very seriously.
Document verification is an important process that is required to confirm the identity of the person. Banks, airports, and government offices all require document verification before they can allow a person to use their services.
Identity theft and fraud are some crimes that can be committed using stolen personal data.
Document verification is one of the lines of defense against these crimes. OCR is used by the systems in banks, airports, and government offices to quickly scan and digitize a document which is then checked by computers against an existing record for any mismatches. Pair this with security questions and it makes committing fraud and identity theft a lot harder.
6. Document Flow in Healthcare
Using OCR, documents such as a patient’s medical history and prescriptions can be digitized easily and fed into the system. Doctors and nurses can easily retrieve these digital records whenever they require them in the future.
Additionally, all other records of tests such as sugar level tests, X-rays, payment records, and other test records can be digitized and fed into the system.
This eases the document flow in a hospital and can make the processes of checking the patients and then releasing them much faster.
This also has the added benefit that when the patient visits the hospital again, even after many years, their history and records will still be there. This can help diagnose their current condition as histories can shed a lot of light on why certain ailments have occurred.
OCR stands for optical character recognition. It is an application of artificial intelligence that allows computers to recognize and extract text from non-Unicode sources.
OCR works by scanning a document or an image, cleaning the image to remove any excess noise, and then converting the image into black and white. This allows the characters to stand out against the background and become easily recognizable. Then machine learning algorithms are used to recognize the characters. They are then extracted and saved in an editable file format.
OCR has many advantages as it can help in automating the workflow, increase productivity, save on costs for hiring professional transcribers and data extractors, increase efficiency and accuracy, improve customer service, etc., etc.
It has many real-world use cases such as in traffic monitoring, document verification, text-to-speech systems, and digitizing physical documents in old companies.
It is a great technology that should can be used in many instances and people should make an effort to make better use of it.