Optical character recognition, usually abbreviated to OCR, is a system that automatically translates scanned images of handwritten, typewritten or printed text into machine-encoded text.
Using Artificial intelligence (AI) & Machine Learning (ML), Cognitiv’s Multilingual OCR solution supports more than 100 languages and will be able to convert handwritten words to it’s digital counterpart.
OCR helps businesses in increasing the effectiveness and efficiency of the work. It’s ability to quickly search through enormous content is extremely helpful, particularly in office settings, which deal with high document inflow and high volume scanning.
Any firms and enterprises planning to do away with paper documents can leverage OCR data entry. OCR can also be used to transfer important documents to tablets, computers, smartphones, etc. Right from businesses in banking, mortgage, and financial sectors to those in legal, payroll, and healthcare sectors anyone can leverage this tool.
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OCR helps businesses to achieve higher productivity by facilitating quicker data retrieval when required.
The time and effort which the employees were required to put in for extracting relevant data can now be focused on more important activities.
Moreover, employees do not have to make numerous trips to central records room to access the required documents, as they can access them digitally.
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Quickly access certain personal or order-related information of the customers to process their requests.
Quick data accessibility becomes extremely important in such cases. OCR helps in systematically storing and retrieving the documents digitally at insane speeds.
With this, the waiting time will be drastically reduced for the customers, thereby improving their experience.
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Scanned documents need to be edited most of the time, especially when some information must be updated.
OCR converts data to any preferred formats such as Word, etc., which can be easily edited.
This will be of great help when there are contents which have to be constantly updated or regularly changed.
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