Optical Character Recognition (OCR) has been a hidden backbone for business processes for many years. But, just how much has the market developed? And, what new innovations are coming to the forefront? Let’s dive in.
The Growth of OCR
Over the years, OCR has grown in both functionality and popularity across a wide variety of industries. For example, in online casinos, OCR is used within live gaming to ensure fairness and authenticity. When players log in to play blackjack at Paddy Power they will join a live stream of a real dealer at a real blackjack table. OCR can then be used to capture the results in real time, the data of which the platform can be used to ensure compliance and fairness of the results, as well as broadcasting the results on screen to anyone who might have missed it. Elsewhere, OCR can be employed in a wide range of office applications, digitizing anything from invoices to resumes, and transforming printed data into machine readable text.
With so many applications, in 2022, the global OCR market was valued at $11 billion, and is expected to grow at a compound annual growth rate of 12% between 2023-2032, according to Globe News Wire. It is estimated that the market will reach $34.16 billion by 2032. The segment of the market that is particularly thriving is solutions, which sits at 57% of the market share, generating $6.27 billion in revenue. This is particularly the case with B2B solutions, which held 64% of market share and a revenue of $7.04 billion. With such a burgeoning market, innovations are rife in the OCR sector. But, what are the latest innovations disrupting the industry?
Handwritten Text Recognition
Whilst OCR has effectively been able to read typed and printed documents for what feels like eons, it has notoriously struggled with interpreting handwritten text. However, thanks to the development of AI and machine-learning software, the development of handwritten text OCR has renewed hope.
These AI-driven solutions are able to learn hundreds of handwriting styles, including different languages, icons, characters, cursive, capitals, and the artistic style itself. The program can effectively learn what certain letters look like in different handwriting styles, which allows the OCR solution to read handwritten notes or addresses more effectively.
Comprehensive Packages
In today’s day and age, there is an increased emphasis on getting results instantly, streamlining, and efficiency. Whilst OCR is pretty common, in more complex applications the code can be quite difficult for researchers and developers to use. Though there are solutions that can simplify specific aspects of OCR, this can make the process more fragmented, as each section has to be considered separately.
According to MarkTechPost, the solution is a comprehensive OCR toolkit, which uses a versatile Python package. The toolkit has modules to deal with datasets, integrate with frameworks, and access utilities within one package, which provides a more simplified and practical approach to dealing with complex OCR tasks.
As you can see, OCR is a critical element of many processes, even if we can’t see it. This has led to the global OCR market skyrocketing, thanks to the movement towards automation and digitisation. And, with AI-based handwriting recognition and integrated detection frameworks solving key pain points, it is likely to continue to grow in the years to come.
