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Advanced AI-Powered Optical character recognition (OCR) uses Natural language processing (NLP) to deal with variations in handwriting which is not possible with Traditional OCR. Traditional OCR has low accuracy as compared to AI-Driven OCR due to its zero flexibility. 

We are now all digitally connected. We rarely transmit handwritten documents and instead prefer to use printed materials. The simple reason for this is that we have become reliant on our computers to process our data. For all these reasons, we’ve decided to turn physical papers into digital files so that machines can quickly modify, find, and store them. We were able to do so thanks to optical character recognition.

What is OCR?

OCR technology, a branch of artificial intelligence that excels in extracting text from photos Extracting text entails making it computer-readable. It takes the handwritten text and converts it to a digital format. An OCR system scans the text of a physical document and converts it into a code, which is then used for data processing.


Converting text from physical to digital was a manual process before the invention of OCR Technology. A manual effort requires a human to rewrite the entire document, which is time-consuming and prone to errors. OCR was already widely used in the 1990s, but it was a manual procedure.

Thanks to AI, OCR is now considered a revolution. OCR has evolved into a human error checker as well as a text recognition program. OCR with AI is available from a variety of software companies. As a result, the text is understood by OCR software. It means that AI systems can detect faults without the assistance of humans. When OCR is combined with AI and Machine Learning, software developers will be able to use computers to more precisely transform text and check for errors. Although AI can understand handwritten documents, the distinctiveness of individual handwriting offers a challenge. As more training data is collected, machines are gaining ground on handwritten papers.

How AI Is Implementing OCR Technologies:

AI is improving the capabilities of OCR tools. Text recognition was the name given to OCR in the 1990s. Physical papers must be manually converted to digital files. Since then, the quality of OCR solutions has grown, and OCR’s usage has broadened thanks to AI. OCR has improved in accuracy and speed as a result of recent improvements.

Machine learning and computer vision techniques were deployed by AI experts. During pre-processing, these items are utilized to examine document layout. The text from the scanned document is then extracted using an OCR engine. The documents were then translated using a deep neural network. We perform this process in real-time to assure accuracy.



TRADITIONAL OCR: Traditional OCR converts text to data and extracts data using templates automatically. These templates either use if-then logic to tell the software where to seek specific data or contain predetermined data locations. Setting up these templates for various businesses involves time and money, as each option comes with its own set of criteria. Because they lack adaptability, they have low accuracy, which is especially true for bills with a lot of variety.

The following are some of the drawbacks of traditional OCR:

  • The positivity that isn’t based on the fact
  • A table of data
  • The image quality of the overlapping text is outstanding.

AI-DRIVEN OCR: AI-Driven OCR recognizes contextual information and can deal with a variety of document variants using Natural Language Processing (NLP). Handwriting is now being converted to text via machine learning.

Artificial intelligence (AI) is striving to simulate the behavior of human brains. AI-Driven OCR acquires, processes, and enters data into computers instead of relying on human procedures like standard OCR. AI takes into account all available data and seeks to make the best conclusion feasible

Applications of Ocr:

 OCR services can help any company that is seeking to handle physical documents. Here are some examples of how OCR can be used.


OCR technology checks the authenticity and amount of the cheque you’re trying to deposit. Without the assistance of AI, OCR technology would not be as evolved as it is today; it lowers errors and improves conversion accuracy. The main disadvantage is that it lessens the administrative and cost load on businesses.


All retailers utilize serial numbers to represent their items, and they use barcode scanners with OCR to retrieve serial numbers from these barcodes at retail shops and warehouses.

Word Processing:

The most popular and early usage of OCR, which we utilize to turn text into editable and searchable data, is word processing. The accuracy of AI is improving.

Historical Preservation: 

With the use of OCR, users can turn historic documents into searchable pdfs, preserving newspapers, letters, and other historical records.

Illegal Documentation:

OCR is used by banks and numerous government agencies to save legal papers in electronic databases for easy access.


 OCR software attempts to recognize characters by comparing shapes to those in the software library. By using character proximity to identify words, the software will attempt to recreate the original page layout. Scanning high-quality originals with sharp, clear scans can result in high accuracy, but this reduces as the original or scan quality degrades.






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