Limitations of OCR and How to Overcome Them

Optical character recognition (OCR) is known for its uses in converting physical text into machine readable formats.

22:59 12 June 2024

Optical character recognition (OCR) is known for its uses in converting physical text into machine readable formats. However, it is also known that OCR has its limitations. So, let's take a look at some of the common struggles of OCR and how you can overcome them. 

Coloured Text and Backgrounds 

You'll probably notice that most documents or text use a white background and black characters. Whilst OCR can easily understand black and white images, it can sometimes struggle with coloured text and backgrounds, which can cause some discrepancies. However, OCR has long since been used in online casinos, particularly for things like Betfair live casino to capture the results of live table games in real time. The results can then be displayed as graphics on the live video feed for anyone who missed the results, and stored by the platform to ensure the fairness of the gameplay. 

Of course, games like blackjack and poker use playing cards, which have red characters on white backgrounds. And, roulette has white numbers on either black, red, or green backgrounds. The key to ensuring the accuracy of OCR with colour is to use image processing techniques in conjunction with the OCR. Things like layer separation and image segmentation helps to determine the characters in the image, making it easier for OCR to pick out the characters. Using these techniques popularised by online casinos, this helps to reduce the risk of error or inconsistencies in the OCR software. 

Unusual Characters 

OCR is typically programmed to recognise the Latin alphabet and numbers. However, some businesses may deal with mathematical equations, scientific symbols, handwritten documents, or languages that use different writing systems such as Arabic, Chinese, Devanagari, and Bengali. It is a known limitation of OCR that it can sometimes struggle with characters that are not seen as 'standard'. 

If this is the case, it is worth looking into an OCR software that has been tailored for your industry, and so is programmed to read the characters you regularly deal with. If this is not feasible, you can customise the OCR application in the settings, or use a platform that combines the technology with AI so that it can learn the new languages required for recognition. 

Blurry Image Capture 

As the name suggests, OCR requires the captured image to be readable. If the image is of a low quality, OCR can have trouble detecting the text on the page. The issues here could be varied - low contrast, poor lighting, and low resolution tend to be the most common issues. Of course, the simplest solution could be that the lens has got dust or smudges on it; a simple clean of the lens could solve the issue quickly. 

If this does not solve the blurry images, a good consideration is image pre-processing. As noted in JCTN, the goal of image pre-processing is to improve the image data. This is done by removing distortions and enhancing the key features. Other techniques include straightening, focusing, and noise reduction. These techniques can bring the image up to a higher quality before it comes to OCR, making it easier for the OCR software to pick up the characters in the image. 

As you can see, OCR is a very beneficial tool for reading characters and transforming them into machine readable documents. However, it does have its limitations. That said, these limitations can easily be overcome with some patience and customisation.