Text recognition 4.0 with OCR

Text recognition 4.0 with OCR

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Text recognition 4.0 - challenges & opportunities of OCR for process automation

Text recognition 4.0 - challenges & opportunities of OCR for process automation

OCR (Optical Character Recognition) has long played a subordinate role in the business world. Only the triumph of digitalization and process automation has brought OCR increasingly into the focus of many companies. The industry has been growing by up to 20% annually since 2018. You can find out all about the challenges & opportunities of OCR for RPA in this article.

What is OCR?

OCR software existed long before automation became a hot topic. OCR stands for Optical Character Recognition and describes electronic systems that can recognize text in images and scans.

According to historian Herbert Schantz, the first OCR system is already 100 years old:

  • In the wake of World War 1, Emanuel Goldberg developed a machine that could convert written text into telegraphic code.
  • This machine was so successful that Goldberg subsequently developed it into the first business solution. At that time, companies still archived data on microfilm, which made viewing the archive extremely time-consuming. Goldberg built a machine that automatically searched microfilm for specific character strings.
  • However, OCR was long limited by fonts. For each font, the OCR tool first had to be trained with corresponding images. It was not until the 1970s that an OCR tool was developed that could recognize almost all fonts.
  • With the advent of the home computer, the first OCR tools for the PC appeared in the 2000s. They allow users to scan texts, for example, and then turn them into readable PDF files.

Data types and OCR

OCR was originally developed for processing structured data. However, other data types are at least as common in modern companies:

How can semi-structured and unstructured data be processed?

Capturing semi-structured and unstructured data from invoices, application documents, ID documents and emails requires an intelligent solution that can cope with different data types and formats.

Template-based OCR technology marks a significant advance in the further development of OCR technology. Using a template, the OCR program extracts the desired information at the desired location in the document. Template-based OCR software thus already includes a step towards automating data processing: no employee has to filter the essential information from the document. Instead, the software only outputs the correct data from the outset.

Modern OCR tools go further by combining electronic text recognition with AI technologies. Intelligent OCR technology relies on machine learning algorithms and works according to this scheme:

  • Digitization and classification of the document using OCR and e.g. keyword classification
  • Extraction and validation of data points from the document using specifically trained AI
  • Verification of the extracted content by a human employee
  • Further processing of the extracted data points in target systems
  • In addition, the validated and successfully read documents are used to train the AI to be even more accurate in the future.

An intelligent OCR solution can therefore be used for structured, semi-structured and unstructured data and offers a number of advantages:

  • Automatic recognition of document patterns and training of these patterns for future automated data extraction of semi-structured documents such as invoices or order confirmations
  • Better recognition of character strings and thus avoidance of errors, for example with dates
  • Machine learning for autonomous training of specific document types
  • NLP for recognizing relevant data points in unstructured documents
  • Freely configurable or predefined form templates that can be used to extract specific data points from structured documents (example: ID card, medical certificate)

Three use cases for OCR & RPA in the company

With advances in the field of machine learning and speech recognition, OCR and RPA can fully exploit their strengths and enable hyperautomation: the automation of complex end-to-end processes. Microsoft Power Automate, ABBYY and UiPath, for example, have modern OCR software as automation platforms that can also recognize semi-structured and unstructured data and map complex workflows.

The introduction of machine learning technologies in OCR software and RPA opens up a wide range of new use cases for all companies.

Conclusion: OCR & RPA

OCR technology has long led a niche existence within the business world. But the integration of machine learning into OCR technology shows how great the technology’s potential is for process automation. Analysts expect double-digit growth rates over the next 8 years and thus a doubling of the OCR market. Companies can already benefit today. RPA suites such as Microsoft Power Automate or UiPath offer a powerful OCR solution in combination with artificial intelligence that can be used to automate initial workflows easily and effectively.