Why Machine Learning-based OCR beats traditional OCR hands down

Gordon Gekko aka Michael Douglas in 1987 in Wall Street with the brick from a cell phone on the beach: A picture that went around the world at the time as never seen before and was considered a paradigm shift. Suddenly it was possible to transact million-dollar deals on a sandy beach with a romantic sunset in the background. Today, when the smartphone in our pocket has more going for it than most computers of fifteen years ago, this image serves only nostalgic purposes. What does Gordon Gekko have to do with OCR (Optical Character Recognition)? In view of technological advances, quite a bit. Just as the cell phone has evolved from a brick to a smartphone, other areas of technology, such as OCR for efficient document extraction, have undergone monumental changes and improvements.

Machine learning-based OCR versus template-based OCR – a battle that is quickly being decided

In the beginning, it was all manual work. Gradually, with the first OCR attempts and rule-based OCR, document capture has tasted blood, seen quite a bit of change, and made more than remarkable technological progress all the way to today’s machine learning-based and thus intelligent OCR. But where exactly are the trend-setting advantages and added values for companies in document extraction with machine learning-based (ML-based) OCR compared to conventional OCR?

Only with ML-based OCR is data extraction from complex and unstructured documents painlessly possible

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Machine Learning-based OCR as a forward-looking direction for companies

Companies are becoming more demanding in all respects, which means that the requirements are also becoming greater and greater, which in turn challenges and promotes the development of existing solutions. For example, companies today require different approaches to their document processing than was the case ten years ago. Simply because the volume of documents to be processed has multiplied exponentially (and the amount of data will continue to increase in the future), the requirements for companies have changed fundamentally. In addition to the volume, there are also largely unstructured documents in a wide variety of forms and formats that need to be processed. Conventional OCR, which is based on rule- and template-based capture, cannot cope with this diversity, which means business should look for alternatives. Quickly! To still use conventional OCR is like using Gordon Gekko’s cell phone again…

The larger the amount of data, the better the ML-based OCR learns – priceless advantages in efficiency compared to template-based OCR

ML-based OCR as a game-changer for document extraction

Of crucial importance is the precise extraction of relevant data from documents. And this from all kinds of unstructured formats and in combination with, for example, handwritten and electronically captured, with checkboxes and fields. Only then can companies automate their processes extensively and straight-through. This is exactly where ML-based OCR comes up trumps in a big way. Thanks to machine learning, it can extract unstructured data more efficiently, analyze it, learn from that data and all previously extracted data (and thus immense amounts of data), and make automated decisions about how to perform a task or process based on that. In detail, the advantages of document extraction with ML-based OCR are as follows:

  • Unstructured, semi-structured, or structured, ML-based OCR takes documents as they are
  • Individual items are easily identified, captured, processed, and extracted by ML-based OCR
  • Faster turnaround times are possible, resulting in increased productivity with the same time resources
  • High flexibility thanks to simple and fast scalability
  • Minimal coordination effort for employees (no maintenance costs)
  • “Document type factory” in the cloud
  • Employees can deal with highly complex scenarios, interventions are limited to exceptions
  • Excellent quality of data preparation and extraction, resulting in overall time and cost reduction in processes
  • Extracted data provides deep insights and can be used for any further analyses

Machine learning-based OCR not only captures and extracts the information, but also interprets and understands its content at the same time

ML-based OCR is a no-brainer

The precision, quality and efficiency of machine learning-based OCR is impressive, the future of document extraction is promising and the integration for companies is an absolute no-brainer.

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