6 Clearly Visible OCR Limitations

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This strategy seems reasonable on the surface, but it’s crucial to comprehend how these technologies operate and their inherent limits. Optical character recognition (OCR), is used to extract crucial information from identification documents like a passport or driver’s license. Typically, this will contain a person’s name, address, birth date, and ID number. In most cases, the data extraction procedure is quick and eliminates the need for manual data entry. Image to text converter online helps to turn your graphic data into text.
OCR, however, is not without its difficulties.

6 Clearly Visible OCR Limitations

OCR was not designed to extract important data fields from ID documents with small fonts and various colored backgrounds that may include holograms, watermarks, and printing on glossy surfaces. Instead, it was designed to read black text against a white background, frequently using a flatbed scanner.

You will come to know about the clear limitations of OCR identity verification in this article.

Let’s get started!

Webcams are a Challenge for Traditional OCR:

For businesses looking to provide an omnichannel experience by enabling customers to capture ID documents across a variety of channels, OCR creates another challenge. The webcams built into PCs and tablets are not as high-quality or capable of taking photos in high resolution as the cameras implanted inside the majority of smartphones. The quality and sharpness of the ID picture will be affected if a company allows its users to verify their IDs through a webcam, which will complicate the capacity of OCR to correctly extract the data. And if you add the use of an image to text converter online in your work then you can easily extract images into text.

More Than OCR Is Required for Data Structure:

There are several phases involved in extracting and structuring the information from an ID document that consumers take a photo of with their smartphone or camera. Finding out exactly what kind of ID document is present is the first step.

In order to appropriately arrange the information read by the OCR and determine the first name, last name, date of birth, and any other fields of interest, this is necessary. image to text converter online helps to clearly provide readable text from blurry images. Straight OCR devoid of additional AI or technology trained to recognize different ID kinds would fall short of the accuracy required to fight fraud and provide a positive user experience.

IDs with Colored Backgrounds Can Be Problematic for OCR:

In order to eliminate blurred text and improve the separation of black and white text from its background, OCR frequently needs to transform a color/grayscale shot to plain black and white. And the use of image to text technology helps you in this regard to convert image to text online without wasting time.

OCR May Be Challenged by Some ID Subtypes:

Given the variety of ID subtypes, mastering OCR can be difficult because it is based in part on a thorough understanding of the patterns that define a particular ID type (e.g. some printed in landscape, some in portrait mode). OCR can only be used to its greatest potential if the data collected is appropriately formatted, which requires that the software be capable of understanding all the complexities and subtleties of the various ID types used around the world.

Here if you are facing some text problems then you can use an image to text converter online it will help you to extract text from image.

Glare and Blur Can Cause Mistakes:

What happens if the user moves slightly or there is glare when the ID image is being taken? The likelihood of making a mistake in the data extraction process is greatly increased when the ID image is glare- or blurrily lit. So you should use image to text technology so that it will be easy for you to extract perfect text so that it will be quite easy for you to complete your work rapidly and without any mistakes.

OCR Must Combine with Image Rectification:

When consumers use their smartphones or webcams to snap pictures of their ID documents, the images typically need to be counted if the image was not properly aligned and reoriented so that the OCR’s tool image to text system can extract the data properly.

Some ID Subtypes May Present a Challenge to OCR:

Given the variety of ID subtypes, mastering OCR can be difficult because it is based in part on a thorough understanding of the patterns that define a particular ID type (e.g. some printed in landscape, some in portrait mode). OCR can only be used to its greatest potential if the data collected is appropriately formatted, which necessitates that the software be able to comprehend all the complexities and subtleties of the various ID types used around the world.

These practical restrictions on extracting information from images of identification documents frequently go much beyond OCR’s initial design. Unfortunately, OCR is frequently not configured to handle these situations, which restricts its usefulness as a standalone engine.

Traditional OCR Struggles with Webcams:

By allowing customers to scan ID papers through several channels, OCR presents another challenge for businesses wanting to provide an omnichannel experience. While the majority of smartphones have high-quality cameras that capture images in high resolution, laptops, tablets, and other electronic devices do not. The quality and clarity of the ID picture will be affected if a company allows users to verify their IDs using a camera, which in turn makes it more difficult for OCR to correctly extract the data.

Bottom Lines:

These practical restrictions on extracting information from images of identification documents frequently go much beyond OCR’s initial design. Unfortunately, OCR is frequently not configured to handle these situations, which restricts its usefulness as a standalone engine. But with the use of the image to text converter online, you can make your work easy because it converts your images into text flawlessly.

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