Before OCR and AI existed, optical character recognition (OCR) was already widespread in the 1990s. OCR was able to help business owners automate the processing of physical documents. It allowed companies to scan documents such as invoices into software and make digital copies.
Today, OCR platforms are still used to convert handwritten printed text into machine-encrypted text so that it can be accessed on a computer. They allow you to manage a wide range of data, such as invoices, business records and financial records.
If you have ever converted text to PDF using a program like Adobe Acrobat, you probably used OCR. The quality of OCS has continuously improved since its inception and is now one of the most powerful instruments in the world.
Unfortunately, modern companies’ demand is quickly outpacing this growth, and companies are beginning to focus on artificial intelligence – driven alternatives to increase efficiency and become more important. Simply creating a document template is no longer enough, companies also want insights, so they turn to OCS as an AI-driven alternative.
OCR needs rules and templates to ensure that the technology actually captures the necessary data. A long and expensive setup process means that every single change requires a new rule, and OCR requires rule templates.
There is also the error stream that can arise if one does not have the flexibility in terms of variability in the document. OCR technology cannot be fully automated, more and more rules must be shortened, and there cannot be as many rules and templates as there used to be.
The growth of artificial intelligence has led modern companies to increase the level of automation that can be achieved, and OCR has been introduced as a means of automating manual business processes.
While AI-based OCR tools may not be as glamorous as other transformative technologies, they have the potential to be included on corporate balance sheets. The combination of AI and OCR has proven to be a key factor in the success of companies such as Google, Facebook and Microsoft. Using AI to troubleshoot leads to a significant reduction in time and effort for employees, as the Ocr engine must be managed by a human user.
OCR tools and artificial intelligence are a key element of the future for the sleeping giants in the digital transformation field. OCR and have the potential to help countless organizations on their way to more efficient and efficient workers
A.I. allows the OCR system to take into account all available resources and find connections and correlations between data structures, creating an organic knowledge pool that adapts over time. This “knowledge pool” provides information on the progress of data extraction and allows for a better and more accurate extraction process.
The A.I. powered OCR system is an excellent first step if your business has problems using the data it collects. With machine learning and OCR, you can focus on responding to the data collected, rather than how it is collected and how it is collected. The reliable acquisition and transmission of data by the software gives you an overview of the company from top to bottom.
Machine learning allows the system to do this with exceptional accuracy in the blink of an eye, and there is no doubt that it can adapt to changing conditions. By combining technologies such as machine learning, artificial intelligence (AI) and machine memory, the Zapbot OCR system is able to learn new languages and adapt to changing document types.
With artificial intelligence, such reports would no longer have to be managed by individual staff or checked by translators. By integrating AI into the OCR solution, Zapbot was able to minimize the administrative burden on investment firms and employees. There are a number of ways in which companies use AI and O CR in their business processes. Documents can be translated, for example, with a Deep Neural Network that uses live data to ensure accuracy. Book a consultation with us and experience a FREE TRIAL of our AI-based OCR and document extraction software!