Text recognition 4.0 with OCR
Text recognition 4.0 with OCR
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:
Structured data is data that corresponds to a standardized format. This includes, for example, a country’s identity documents: The German ID card always has the same structure. The same information types (name, address, number) are in the same place in the same format. Only the content differs. Structured data can be processed by software robots using simple rules. For example, a simple OCR solution can recognize the data based on its position on the document once it knows the pattern of the ID card.
Data is semi-structured if the information types are uniform, but the position in the document is not. Invoices are an example of semi-structured data. 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 always produce an error if an invoice deviates from the assumed structure.
Data is unstructured if it differs both in the type of information and in its 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, 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.
KYC processes are required by law in the financial sector. Companies must verify the identity of their customers before they are allowed to give them access to their platform. Without RPA, this requires an enormous amount of resources: employees have to request the data, check it manually and authorize 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 results and assigns them to the information types
- The software robot activates the user
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 also includes invoices. Invoices are semi-structured data and must therefore be prepared for archiving and entered into the archive. This process can be made much more efficient with automation:
- The software robot monitors incoming emails and searches them for invoices
- The invoice is read out using OCR
- The AI extracts the relevant information
- The invoice can then be validated and approved
- As soon as the invoice has been paid, the robot automatically moves the invoice to the correct location in the archiving system
Filling vacancies takes a lot of time. Incoming applications have to be recorded, sorted in several stages and distributed to the decision-makers before HR contacts all applicants manually and arranges interviews. Automating this process could look like this:
- Applicant uploads their application via an application form in the digital application portal
- Intelligent OCR software analyzes unstructured, semi-structured and structured data and prepares it for the software robot
- A software robot can now 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 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.
