Implications of ML-based document extraction on robotic process automation software

Machine Learning (ML), Computer Vision and Robotic Process Automation (RPA) are some of the most hyped words in tech space today. Why shouldn’t they?! Because the impact that the technologies behind them are already having in a wide range of business areas is remarkable. They lead to streamlined business processes, cost reductions, intelligent solutions or further improvements, faster decisions and various other advantages.

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How is RPA beneficial to organizations?

RPA enables companies to configure so-called “robots” that can mimic human actions and workflows. The technology thus helps companies to automate everyday tasks and rule-based, static, repetitive processes. This has several obvious advantages:

  • The automated systems are much faster and therefore save time and money
  • Time savings allow management to use these released resources alternatively for other higher-level work that requires creativity and other human skills
  • Processes are less error-prone than manual work
  • RPA is usually easily scalable and is therefore suitable for organizations that deal with many data and information-based processes

These advantages have led to widespread use and popularity of RPA. They are used, for example, to log in to applications, move files and folders, copy and paste data, perform calculations, browse websites and extract relevant data as well as extract text from documents, PDFs, emails and forms. In addition to these exemplary use cases, however, there are also a number of other possible applications where RPA is predestined.

Optical Character Recognition (OCR)

It is quite obvious that document extraction and RPA have benefited greatly from each other. This is due to the fact that OCR can be used to automatically extract relevant information from documents of different types, such as invoices, balance sheets, legal documents, bank statements, tax returns, etc. To do so, OCR technologies use visual techniques to scan an image for borders, fonts and characters. By using complementary techniques such as neural networks, they recognize the characters and also use linguistic concepts of Natural Language Processing (NLP) to recognize words and semantics. These technologies are therefore relevant for RPA, as they can be used to partially or fully automate additional steps in various processes. And since automation is top of mind at many companies, a correspondingly high level of investment, research and work is being done in this direction.

An example to support the relevance: Imagine a person trying to read the various small pieces of data from thousands of invoices and copy them into the database. Not only is this done very slowly and as a task very boring, but it is usually quite error-prone too. Thanks to the possibilities of OCR, tasks such as data entry have become largely automated and more precise. Instead of manually searching for a specific text in a 100-page document, these programs can scan the document in seconds and retrieve or output the contents in no time. Admittedly, not always error-free.

OCR Challenges faced by RPA developers 

Like any other technology, OCR has its problems and reveals a certain complexity to users. The most common problem is incorrect character recognition, which can be due to several factors:

  • Poor quality of the scanner, resulting in spots and uneven contrast on the document
  • Rescanning a previously scanned document
  • Incorrect page orientation in the document
  • Presence of watermarks, stamps and handwritten text on the document
  • Crumpled and/or faded documents
  • Special text formats with different blocks and pagination

If the document or scan has any of the above features, we may not be able to achieve the desired level of accuracy. For example, this may lead to cases where the OCR engine recognizes a “5” as an “S” or the letter “O” instead of the number “0”. For documents that contain tabular data, such as invoices and balance sheets, it becomes difficult to see the boundaries of the columns, which can lead to incorrect data assignment. 

As you can see, such situations have negative effects and are sticking points in the architecture, development and operation of RPA applications. For example, if a decimal point is missing and $400.00 is read as $40000, this can have serious consequences. This is because normally several other steps are based on the results of document extraction and sometimes for a long time – which would be the case in an ideal world at least – human supervision is missing respectively not intended. Inaccuracy at this early stage can therefore pose a serious challenge for the downstream processes that process the data read in. Add to this the fact that there are thousands or even millions of such documents in a company that need to be processed continuously. Errors at the beginning of the process are transferred to the downstream processes and will therefore lead to errors in the document management system (DMS), ERP or other leading systems. In short: OCR-based RPA applications are often less robust and face the need for human intervention, i.e. they are slowed down in terms of processing time. And yet, it is an indispensable way to capture data from different media in a system at low cost.

How can these challenges be overcome?

You see, at this point – at least today – human post-processing is indispensable. Now, you have the option of either developing and introducing internal processes yourself so that your employees can check the extracted metadata of your OCR for errors and make corrections (in this context also called annotation) or you are looking for a dedicated provider who is much more efficient in these matters due to specialization and therefore decide to buy these services instead.

With Parashift, you can get all this out of one house. That means market-leading OCR software on the one hand, but also efficient and scalable annotation of extraction results on the other hand. In addition to the efficiency gain that this offers, a further advantage is that, apart from always having correct data, you also benefit from continuous improvement of the extraction software, for which you do not need to make any expensive investments nor plan projects compared to when you would go with conventional OCR providers. By the way, the improvement of Parashift’s OCR is not limited to accuracy, but also relates to the ever-expanding support of different document types. Specifically, Parashift aims to standardize all sorts of common everyday business documents so that you can simply upload a contract, for example, and immediately receive the most important data from it back in perfect quality. Configuration and training should no longer be necessary. To learn more about this, read this article here.

When it comes to the implementation, optimization and scalability of our document extraction solution, the cloud-based OCR software offers numerous advantages. This because the open and extensible platform allows immediate access to functionalities and learning based on an extremely large number of documents, there is no need for costly and lengthy projects until you can start with normal operations. In addition, the software is easy to integrate into all kinds of business software due to simple and well documented APIs. Thus, legacy system projects are comparatively extremely costly and not really future-oriented.

In summary, OCR technologies have come a long way to be increasingly reliable in extracting information correctly from documents, no matter how unstructured. However, even though these solutions are now powerful and can reduce costs many times over, certain accuracy challenges remain. In this respect, efficient human intervention is both useful and necessary to avoid errors and improve the capabilities of the extraction machine in a sustainable way.

As mentioned already, we combine exactly these two dimensions at Parashift: Top-notch machine learning technologies and human post-processing. This to create a flexible and efficient solution that can meet any document extraction challenge and always deliver excellent quality data. Something that no other OCR provider can deliver today. Try it out for yourself!

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