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Saves paper and storage space since more data can be converted to electronic formatĪ typical example of leveraging OCR and natural language understanding to extract text from image software like KlearStack can be seen in medical insurance claim form processing.īy extract text from image software makes it easy to compare the insurance claim with the policyholder’s details.

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Reduces manual data entry, which indicates reduced overall costs for the business.

Restricts the need for manual data entry.

Saves time and reduces the scope of manual errors.

Faster, automated processing and conversion of paper-based documents into digital formats, which accelerate workflows.

Here are some of the significant benefits of OCR and natural language understanding to automate the extraction of values from images: This is crucial for industries like banking and insurance as they have to deal with media and documents daily. A computer can easily read this additional layer, making the image recognizable and searchable. The primary benefit of OCR technology is that it automates manual and time-consuming data entry tasks.īy using OCR, one can create digital documents that can be edited and stored per requirements.Īn OCR tool processes images to identify the text and creates a hidden layer of text behind the image.

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Several software and tools, offline and online, allow OCR technology to extract text from images, KlearStack being one of them! More straightforwardly, OCR converts digital data in image format into editable word-processing documents.

These can reconstruct the extracted text in a machine-readable format, due to which the extracted text can be selected, edited, or copy-pasted like regular text.

These tools are specifically trained to identify the shapes of characters or numbers on an image to recognize the text. For text extraction, the OCR tools utilize several machine algorithms for pattern recognition to identify the presence and layout of the text in an image file. An OCR program is a tool that extracts and re-purposes data from scanned documents, camera images, as well as image-only pdf.Īn OCR software uses a combination of hardware, such as optical scanners, and software capable of image processing. ‘OCR’ stands for Optical Character Recognition, commonly known as ‘Text Recognition,’ a popular technique for extracting text from images. Also, we will have a look at the business use cases as well as the advantages. To add to this, up to 40 percent of more strategic activities like financial controlling and reporting, analysis, treasury, and financial planning, can be automated.Ĭoming to our topic of discussion in this blog, let’s understand what OCR is. McKinsey also estimates that 75 to 80 percent of transactional operations, such as general accounting operations and payments processing can be automated. Platforms like KlearStack handle extensive data and provide five-star customer support along with delivering personalized notices. Instead, it helps leverage artificial intelligence to make informed decisions. Today, the scope of OCR in the finance industry, data automation isn’t only about online insurance payments or invoice automation. Extracting text from image software will be at its core. It will also enable banking employees to focus on more challenging tasks.īanking institutions would require complex problem-solving and creativity.

This will increase the capacity of financial institutions. You can find usable language-country codes below.McKinsey has predicted that data automation will fulfill 10-25% of bank business processes within the next few years. You can specify both language and location in your news search using the location parameter.