Optical Character Recognition: What Makes It a Perfect Match for RPA

~ 11 minutes read

Let’s picture a widespread situation. You need to draft a standard agreement or an application form and surf the web to find some proper template. Finally, you have found something appropriate, but the document is in PDF format and is not editable. What would you do?

There are two options available: 

The first one is to make a manual copy of the agreement template. But what if it is ten pages long? Not the best end of the day, right?

Or you can choose the second option, i.e., leverage a special software that converts the PDF document into any required editable formats, saving your time and efforts.

Apparently, most of us would choose the second option.

The technology of converting the various format documents (images, scanned copies, pdf-files etc.) into an editable and searchable format is called Optical Character Recognition, or OCR.

In this article, we will define OCR and the technology that lies behind it, consider the types of OCR and formats typically used within it. What is more, we will show the added value that OCR brings to the business processes while combined with Robotic Process Automation tools.  

OCR: Technology explained 

OCR: Technology explained 

The typical examples of images that are converted by OCR technology always contain typed or handwritten texts and can be in a format of scanned paper documents, PDF files, or images captured by a digital camera.

Though each Optical Character Recognition system works slightly different depending on the developer and the purpose, there are some common steps of file processing that can be implied to all types of OCR software:

Step 1. Pre-processing

The pre-processing step makes the document visually clear and provides it with readability. 

Here are the most common means within the pre-processing step which eliminate imperfections while characters’ recognition: 

De-skewing: providing the proper alignment of the characters

Line removal: cleaning excessive spaces and lines with no loss in the actual data

Binarisation: converting the document to black and white format making the recognition easier

Zoning: separation of different blocks (columns, texts) within the file

Despeckle: smoothing the borders and removing spots if any

Segmentation: segmentation of characters before the actual launch of OCR dividing image artifacts into multiple characters.

Script recognition: identification of various scripts allowing OCR to call the proper script while running and data capture

Step 2. Character recognition

This step implies the separation of each character and recognition of all the pixel characters and spaces. The processing of each character allows the system to recognize the specific groups of characters as words.

Usually, the recognition is based on two techniques:

Matrix matching

The core of this method is comparing an image of a character with the glyph stored. It is best applied in case the fonts in the documents are standardized and common.

Feature extraction

This technique recognizes lines, loops, intersections and other features efficiently contributing to the overall data recognition in the file.

It’s also worth mentioning that almost all OCR systems use variations of neural networks, since, in terms of character recognition, ML (machine learning) is more efficient and reliable than a rule-based approach. 

Step 3. Post-processing

Once the data processing is completed, the software enhances their accuracy. At this step, the accuracy of the final data would depend on 2 factors: the complexity of the OCR system and the complexity of the initial data. For instance, typically, the simple OCR systems store standard fonts in their libraries, and in case the document contains fancy fonts or handwritten text, the simple OCR with standard fonts stored in its library cannot assign the proper metadata to it. In case the document contains complex and non-standardized characters, it would require advanced OCR systems.

RPA in OCR: Why the two are a perfect couple

Now when we have a better understanding of Optical Character recognition, let’s dig a little deeper into Robotic Process Automation, the technology where OCR is extensively used.

Robotic Process Automation, or RPA, is a technology that allows people to deploy ‘digital workforce’ or programmed robots that emulate a human’s actions within computer systems to execute business processes more efficiently. RPA is associated with repetitive, mundane tasks that are usually much time-consuming. 

There is no secret that OCR + RPA is a perfect combination when it comes to automation of the companies’ daily operational routine.

But what makes it so special?

In simple terms, while OCR is used to recognize and read information from various documents, such as printed or scanned accounts, invoices, contracts, images with subtitled texts imposed on them, RPA helps to properly distribute the information to the relevant corporate systems – CRMs or various types of corporate software. RPA also allows for the preparation of financial transactions, building or updating excel tables, sending messages to Outlook, and performing almost any other office routine. All of these actions are based on the data which have been recognized and extracted by OCR.

It should be pointed out that RPA adds value to OCR, as typically Optical Character Recognition is used with highly structured documents, while in combination with RPA, it can process and analyze various format unstructured files. To top it off, RPA bots may adapt to various scenarios and improve data collection and analysis processes, which OCR alone is incapable of doing.

RPA + OCR: 4 use cases 

Let’s see how this perfect couple performs in business reality through 4 illustrative examples from different industries.

Accounting: Invoice processing

The accounts payable and invoice processing, especially when it comes to high volumes of documents, are mundane labor-intensive tasks that can lead to employees’ exhaustion and consequently cause errors. OCR RPA technology can take this burden automating the processing of the invoices once they come in. The process is as follows: apart from recognizing scanned documents, the relevant data go through the system analysis and put into the related data fields in the accounting software or ERP.

Below you could see an illustrative example of invoice processing performed by ElectroNeek OCR RPA within the demonstration of features related to 2.0 platform release:


  • Human error reduced to a zero rate
  • Time spent at labor-intensive, repetitive tasks minimized

Sales: Sales/Purchase orders processing

Sales managers spend hours gathering all client-related information from various systems and input it into the CRM or ERP. The finance and accounting department staff members devote time to copying and inputting the data into the accounting systems. Obviously, this may lead to data duplication and errors, which has a direct impact on the efficiency of the enterprise. 

However, by combining OCR and RPA technology, you can automate such complex sales operations in the areas of sales orders entry, invoicing, etc. 


  • Clients data tracking and storage reached
  • Customer service enhanced

HR: Recruitment process

The incoming CVs from candidates can be quickly sorted and classified with combined OCR RPA technology. By setting up a number of relevant keywords, you can see the list of appropriate candidates’ applications, exclude those who do not fit with the open position, or do any other mundane task within the recruitment process. 

OCR RPA may help the processing of scanned documents from new employees, i.e. it will not only recognize the data but also input it into the relevant Human Resources CRM for further data management


  • Hiring process optimized
  • Onboarding process simplified 

Healthcare: Patients’ data processing

There is a common practice of manual input of data in healthcare once it has been extracted by OCR technology. For instance, in hospitals, OCR may recognize and read the data filled by patients; however, the staff members afterward should manually input it into the CRM. The RPA automates this process, and after the OCR has completed its part, the RPA bot handles data input and eliminates the manual work.


  • Manual data output and input significantly reduced 
  • Patients’ care service enhanced

Learn more on how automation solutions can help in:

Banking, Insurance, Human Resources Management, Marketing, Logistics, AccountingFinance, Healthcare

RPA and OCR: It’s always better together

We have discussed the specifics of OCR and RPA and illustrated potential impacts for various businesses when they are used together. 

In a nutshell, OCR and RPA are two separate systems, both aimed at strengthening the effectiveness of business processes. Combining these two technologies may bring even better results for your enterprise, adding value to your business workflows with no risks related to improper inter-system data storage and entry.

Therefore, while considering business processes automation, it is vital to seek the vendor with expertise and experience both in OCR and RPA. 

By contacting ElectroNeek, you get both a high-level accuracy OCR system built-in into your system and sophisticated RPA solutions which guarantee proper data management. 

Ready to automate your document recognition processes? Book a demo and start breaking new ground today with ElectroNeek!