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OCR Check – A Robust Software to Verify Identity Documents



Companies demand reliable and quick processes for their convenience in today’s technologically advanced world. The researchers are working around the clock to introduce automated apps and tools. Every industry has a vast customer base that needs to fill out certain documents in order to use services. Handling customer documents is a crucial component of any company’s processes. Businesses typically use manual techniques to accomplish this, which are demanding and time-taking. There is a significant risk of data redundancy because of human interference. Therefore, organizations badly want automated software and solutions. When handling financial and customer identity documents, the system should be capable of accurately retrieving the data and auto-filling the content without any mistakes. An OCR check is crucial in addressing this problem and considering the demands of businesses.

How Does an AI-Powered OCR Check Work?

Information that has been written over documents is converted into machine-coded text using OCR software. OCR document verification converts the information written across the pages into machine-coded text. The manual processes of extracting data and form submission are automated with OCR solutions. In order to create a softcopy that one can update or save in the database, it is possible to digitize the content of a document using an OCR check. To increase OCR compliance’s flexibility and sturdiness, the SaaS providers carefully integrated AI into the technology.

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  • Document Processing 

Document De-skewing Technique

This technique correctly aligns the client’s documents and makes sure they are free of any folds or scratches. De-skew is the procedure that does this by aligning the files by turning them slightly both horizontally and vertically so that they exactly fit. The online OCR also eliminates stains and smooths out the edges.


It is the process of converting colored document files into a binary image, which is a standard for greyscale images. It is feasible because an OCR scanner reads binary images more accurately than colored ones. Additionally, it raises the standard of text recognition, which leads to accurate data analysis.

Script Recognition

The meanings of words can vary in papers written in distinct languages, so it is important to identify the scripts before using OCR. This makes it easier for OCR services to retrieve the information.

Character Isolation

In order to receive precise findings from an OCR check, it is usually necessary to distinguish a few of the characters that are typically jumbled together because of image imperfections. Segmentation is the term that describes this approach to character separation. Segmentation arranges the font portions on the grid. The vertical lines cross the black sections of the characters because of the mixture of white space among them.

  • Character Recognition 

Pattern Recognition

An OCR check employs a matrix matching approach to compare the images by positioning them mostly on a graphic pixel by pixel to recognize the document layout. This procedure depends on the inputs being put correctly on the glyph. This works flawlessly on typewritten texts with the same fonts and is highly effective.

Feature Extraction

The document’s multiple linguistic inputs can diminish the ability to identify document patterns. However, this technique breaks the character into smaller fragments in the sort of feature-like lines that cross with closed loops. It then retrieves features represented visually as vectors. This increases the efficiency of the character recognition process as a whole.

  • Automated Data Population 

The data is ready to be filled into the pages once it has been pre-processed through a number of processes. The pre-processed data that was saved in the database is utilized to automatically fill up the client verification documents, which gives the client save more valuable time. The data has to pass the post-processing procedure in order to improve OCR recognition reliability. It is a close neighbor analysis. Both errors and words that need to be typed without spaces are found using this method.

Final thoughts

Customers of digital businesses demand quick and reliable processes because they live in a fast-paced environment. Everyone is just too busy and they do not have the patience to stand in long lines for verification purposes. OCR apps are now essential for businesses since they can simplify the human data-entry process. The company can enhance its pre-processing method and the client experience by using an OCR check. It is undoubtedly a great option for businesses to handle their data efficiently.

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