OCR technology has been vital in building today’s world, where information flows efficiently between devices. That’s because this technology has been crucial in the process of digitizing handwritten and printed information, enabling efficient data entry, and improving search engines.
Since OCR technology can obtain text from scanned documents, it has also been crucial in facilitating accessibility features in devices used by visually impaired individuals. This technology’s ability to read data from physical sources is why it’s also used in many industries. Here’s a look at how OCR technology works.
How OCR Works?
One popular industry that uses OCR technology is iGaming, specifically in the arena of live casinos.
Some of the best online casinos around are regularly ranked by Techopedia, with casino expert Jonathan Askew and others vetting them for things like licensing and legitimacy.
When it comes to live dealer games, OCR technology has become instrumental in enabling this subgenre of iGaming since it can be used to read cards. However, many wonder how exactly OCR technology works.
The first step in how OCR, or Optical Character Recognition, technologies work is called image acquisition, which entails capturing images that contain text. These images can be device screenshots, photographs, and scanned documents, and for them to work accurately as required, images are mostly captured through high-resolution cameras.
If the information that needs to be digitized has already been captured with low-res cameras or is not clear enough on the surface, there may also be a preprocessing stage that happens before or after the image acquisition step to improve readability.
This preprocessing stage can involve removing smudges or any speckles on the picture or document that are preventing data from being readable. Sometimes, an image may even be converted to black and white if OCR readers can’t recognize sections of the text.
After the preprocessing and image acquisition stages, OCR readers go through their text recognition step, where the technology’s algorithms analyze the images to recognize words and characters. They do this by using a process called pattern matching.
In the pattern-matching process, OCR readers compare known character patterns with segments of the image. Once segments of the image have been matched with corresponding characters, this technology then aligns the characters to match fonts and document layouts.
When all this has been done, OCR systems then produce the image in a machine-readable format such as a PDF, word, or plain text document.
Use Cases and Applications
Now that we know how OCR technology works, let us see what this technology is used for and what other industries utilize this tool for in their daily operations.
OCR systems are mostly used to convert and scan documents such as paper documents, news articles, and books since they can scan printed text and convert them into electronic formats.
This is why many organizations use OCR technologies to manage their documents, as they become easier to edit, share, and search once successfully digitized.
Many organizations also use OCR technology to extract important information from physical documents like receipts, forms, and invoices. By using OCR systems on these documents, many companies reduce data entry errors and speed up workflows in areas like accounting, administration, and finance.
Government agencies, educational institutions, and libraries are also using OCR technologies to make digital copies of official records, historical documents, and books as a way of preserving them against unforeseen circumstances like natural disasters.
Nowadays, one of the most notable uses of OCR tech comes from the healthcare sector, which uses it to create digital copies of prescriptions, lab reports, and patient medical records.
These digital copies make the information these documents contain searchable, which helps healthcare professionals make better decisions and provide better care to their patients.
As previously mentioned, OCR systems are also used extensively in online casinos. In these spaces, the technology is used to make physical playing card characters understandable to the platform’s systems and settle in-game disputes as it also records every move the dealer makes.
Lastly, one of the most prominent uses of OCR technology comes from the logistics sector, where these technologies are used to read shipping labels to simplify inventory management and package tracking.
Challenges and Limitations
Even with the various uses and optimized processes OCR systems NOW come with, they still have some challenges and limitations. One of these is accuracy, as it can vary due to several factors, such as language, image quality, and font type.
For example, even though OCR technologies are good at reading printed text, they can struggle with scanning handwriting as people have different writing styles.
Even with printed text, OCR readers may sometimes misinterpret characters that have been typed using cursive or complex fonts.
When it comes to languages, OCR technologies may struggle with understanding the writing conventions of some languages. That is why users should look for solutions that can accurately recognize the characters of the language their documents use.
Recent Trends and Innovations
A major trend that is reshaping how OCR solutions are used is the rise of cloud-based OCR services. These services let users upload scanned documents or images to cloud servers where advanced OCR algorithms process the text they contain.
The rise of these services has been great at improving scalability in many organizations as they can process large volumes of documents simultaneously. The scalability also allows for more cost-effective solutions, as users only have to pay for the work they need to be done.
This process has helped many organizations eliminate their need for expensive infrastructure and specialized hardware by effectively digitizing all their paperwork and records.
Cloud OCR services have also helped software developers integrate OCR APIs into their projects so their sites and applications can have OCR functionalities.
By integrating OCR features into organizations’ systems, developers have made document processing workflows more efficient, allowing these organizations to focus on their core operations.
Another big trend that’s changing OCR solutions is the innovative use of AI. AI has already begun making significant inroads in OCR technology by improving how it recognizes handwritten text, different languages, and complex fonts.
AI-powered OCR systems can do this through their advanced pattern recognition features and machine learning that allow them to analyze data and learn from it so their accuracy can be improved over time.