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. It was not until the triumphant advance of digitization and robotic process automation that OCR increasingly moved into the field of vision of many companies. Since 2018, the industry has been growing by up to 20% annually. Read all about the challenges and opportunities of OCR for RPA in this article.

What is OCR?

Long before automation became a hot topic, OCR software already existed. 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. Companies were still archiving data on microfilm at the time, which made viewing the archive extremely costly. Goldberg built a machine that automatically scanned microfilm for specific character strings.
  • OCR, however, was long limited by fonts. For each font, the OCR tool first had to be trained with appropriate images. It was not until the 1970s that an OCR tool was developed that could recognize almost all fonts.
  • Then, with the enforcement of the home computer, the first OCR tools for the PC came in the 2000s. With them, users can 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 types of data are at least as common in modern businesses:

  • Structured data: Structured data is data that conforms to a standard format. This includes, for example, identification documents of a country: The German identity card always has the same structure. The same information types (name, address, number) are in the same place in the same format. Only the contents differ. Structured data can be processed by software robots using simple rules. For example, a simple OCR solution can recognize the data by its position on the document once it knows the pattern of the ID card.
  • Semistructured data: Data is semistructured if the information types are uniform, but the position in the document is not. An example of semistructured data is invoices. Invoices do not follow a standardized format. The address can be at the top left or in the footer – but it is always on the document. Rule-based approaches reach their limits here and produce an error whenever an invoice deviates from the assumed structure.
  • Unstructured data: Unstructured data is data when it differs in both information types and format. For example, emails, contracts, and logs. Unstructured data is the biggest challenge for modern OCR tools due to the absence of patterns in both type and format.

How can semi-structured and unstructured data be processed?

Capturing semi-structured and unstructured data from invoices, job applications, ID documents, and e-mails requires an intelligent solution that can handle different types of data as well as different formats.

Template-based OCR technology marked a significant advance in the further development of OCR technology. Based on 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 outputs only the correct data from the start.

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 based on OCR and, for example, keyword classification.
  • Extraction and validation of data points from the document using specifically trained AI.
  • Verification of the extracted content by a human operator
  • Further processing of the extracted data points into 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 thus be used for structured, semi-structured, and unstructured data and offers several benefits:

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

Three Use Cases for OCR & RPA in the Enterprise

With advances in Machine Learning and speech recognition, OCR and RPA can play to their strengths and enable Hyperautomation: the automation of complex end-to-end processes. Microsoft Power Automate, ABBYY, and UiPath, for example, have advanced OCR software as automation platforms that can recognize semistructured and unstructured data and map complex workflows.

The advent of Machine Learning technologies in OCR software and RPA thus opens up a wide range of new use cases for all companies.

KYC (Know Your Customer) process

In the financial industry, KYC processes are required by law. Companies must verify the identity of their customers before giving that customer access to their platform. Without RPA, this goes hand in hand with an enormous amount of resources: employees have to request the data, manually verify it, and activate the users. Thanks to modern OCR technology and software robots, this process can be fully automated:

  • The user logs in
  • The system recognizes a login and automatically requests the necessary documents
  • The user uploads the documents via a form
  • An OCR tool reads the data from the uploaded documents
  • The AI interprets the read-out results and assigns them to information types
  • The software robot unlocks the user

Archiving of business data

The GoDB places high demands on the archiving of important business data. Companies in Germany must store relevant data for 7 – 10 years in an audit-proof and documented manner. This includes invoices. Invoices belong to the semi-structured data and must therefore be prepared for archiving and entered into the archive. With automation, this process can be made much more efficient:

  • The software robot monitors incoming e-mails and searches them for invoices.
  • The invoice is read out via OCR
  • The AI extracts the relevant information
  • The invoice can then be validated and approved
  • Once the invoice is paid, the robot automatically moves the invoice to the correct location in the archiving system

Recruiting

Job recruiting takes a lot of time. Incoming applications need to be captured, sorted in multiple stages, and distributed to decision makers before HR manually contacts all applicants and sets up interviews. Automating this process could look like this:

  • Applicants upload their application via an application form in the digital application portal.
  • Intelligent OCR software analyzes unstructured, semistructured, and structured data and prepares it for the software robot
  • A software robot can now further process the applications according to defined criteria and, for example, automatically sort out all applicants with a final grade of >3.0 or higher
  • The software robot automatically sends rejections to all filtered-out applicants.

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 just how great the technology’s potential is for process automation. Analysts expect double-digit growth rates over the next 8 years, doubling the size 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 simply and effectively.

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