The financial institution was looking for a solution that would automate document creation and help to identify high priority loans. Softworks AI, worked with a Fortune 500 financial institution to simplify the management of mortgage documents. There are numerous other examples of companies using AI and OCR in this way. By incorporating AI with an OCR solution, Infrrd was able to minimise the administrative burdens of the investment firm’s employees. Without the help of AI, such reports would need to be managed by individual employees and checked by a translator. The documents were then translated with the help of a Deep Neural Network using live data to ensure accuracy. An OCR engine was then used to extract text from the scanned document. These algorithms were used to analyse document layout during pre-processing to pinpoint what information was to be recorded. To do this, Infrrd used a combination of machine learning and Computer Vision algorithms. The tool was used to copy financial reports from various languages and translate them into English. Infrrd IDC, a hybrid AI and OCR tool was used to help manage financial reports. One detailed case study of how AI is used to enhance OCR can been in Infrrd’s work with a global investment firm. In practice this means that AI tools can check for mistakes independent of a human-user providing streamlined fault management.īut how do these tools work? The answer is slightly different depending on which AI platform you’re is using. As a consequence, data capturing software is simultaneously capturing information and comprehending the content. OCR tools are undergoing a quiet revolution as ambitious software providers combine them with AI. In other words, they can process document content more thoroughly. AI solutions can do this automatically while pulling insights from the text. Under traditional OCR, the user’s only option to increase the reliability of scans is to manually measure and monitor the results. For example if you record an invoice that omits or incorrectly records the name or price, that document is as good as useless. The rate of accuracy is problematic as small mistakes can result in the loss of important data points. For typed text, most platforms maintain a 98 or 99% rate of accuracy. The accuracy of OCR is dependent on the quality of the original document. The moment the user has finished configuring their OCR settings they have an automated solution for creating digital copies of physical documents. After entering this into the OCR platform the selected text is found and then recorded in a digital format like a PDF. The most common form of template-based OCR works by the entering the coordinates of the text they want to record from a physical document. Optical character recognition (OCR) is a technology that allows converting static documents, such as physical forms, into a format that’s searchable and editable. Five ways OCR tech can improve workflow efficiency Simply creating templates of documents is no longer sufficient enterprises want insights as well. Companies are starting to turn to AI-driven alternatives to boost their efficiency and extract meaning. Unfortunately the demands of modern enterprises have fast outstripped its growth. The quality of OCR has steadily improved ever since it was created. If you’ve ever transformed a text into a PDF with a program like Adobe Acrobat, then you’ve used OCR. OCR platforms make copies of documents like receipts, bank statements, passports and other forms of documentation that need to be managed. Today, OCR platforms are still used to convert handwritten or printed text into machine-encoded text so that it can be accessed on a computer. With OCR, enterprises begun to use software to scan documents like invoices and create digital copies. OCR was instrumental in helping business owners to automate the processing of managing physical documents. Combining AI and OCR together is proving to be a winning strategy for both data capture and managementīefore there was OCR and AI working together, back in the 1990s, optical character recognition or OCR was already in wide use.
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