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OCR for RPA: Bots Now Understand Unstructured Data in 2024

Robotic process automation (RPA) bots aim to replicate human interactions with computers in order to automate the performance of tedious repetitive tasks. Optical character recognition (OCR) enables RPA bots to handle tasks that require scanning documents and converting them to a format readable by machines. RPA bots rely on OCR for most document automation tasks including invoicing, resume screening, inventory management, and more.

In this article, we explore how and where RPA bots leverage OCR.

What is OCR in RPA?

Optical character recognition (OCR) is a tool that captures handwritten and printed texts in images (unstructured data) and converts them into characters readable by machines (structured data). OCR works by analyzing patterns of light and darkness that make up different letters and numbers in a certain language. OCR follows these steps:

  1. Preprocess the image by converting it to gray scale, smoothing, and de-skew the letters.
  2. Detect lines that represent characters and words
  3. Create a list of candidates of each character and compare it to the training data
  4. Choose the best fit and produce a machine-readable character
Source: Geeks for geeks

RPA bots leverage OCR to recognize text in images and documents, convert them to structured data, and feed them to the bot for further processing.

What are the use cases of OCR in RPA?

OCR enables RPA bots to extract text from images and scanned documents. Therefore, it is the cornerstone of numerous RPA use cases. For example:

In finance

It has been estimated that ~42% of finance processes can be automated with the right tools. Combing RPA with OCR capabilities enables finance leaders to automate tasks such as:

  • Invoices: OCR captures invoice data by extracting relevant fields such as vendor data (VAT ID, address), and purchasing information (purchased products, prices, VAT rates) in order to create master records, and enable RPA bots to match invoices to purchase orders, and orders to offers.
  • Insurance documents: OCR is used to extract insurance-related data such as passports, birth certificates, driver’s licenses, historical medical records, voting ID, etc. RPA bots require this data to register and process claims, detect fraud, and ensuring compliance.
  • Credit scoring: OCR can be used to capture data for creating credit reports. This data can be extracted from clients’ payment history, tax returns, credit card debt, mortgages, loans, etc. RPA bots use this data for processing and validating loans, and managing credit and debit cards.
How OCR extracts receipt data


  • Resume screening: OCR extracts candidate data from their paper resumes, reference letters, and education certificates, in order to enable RPA bots to automate candidate sourcing and verifying their employment history.
  • Travel and expenses management: OCR can be used to scan employees’ expenses such as cash register receipts, boarding passes, account statements, etc. Thus, RPA bots can cross-check individual expenses against company rules and external expenditure regulations to ensure compliance.
  • Document management: HR produces a significant amount of documents about recruitment, payroll, employee archives, organization policies, etc. OCR enables RPA bots to extract this data and automate the documentation process in the HR department.

In healthcare

It was estimated that the global healthcare industry generated ~2,3 exabytes of new data in 2020, and reports claim that bureaucratic burden is the top reason for physician burnout across generations. Using RPA in combination with OCR enables healthcare workers to automate:

  • Patient registration: When a patient first visits a healthcare facility, they are required to fill a patient entry form with their personal information, which a healthcare employee would manually input into the system. OCR can be used to capture the information from the patient entry form and the RPA bot can enter the data into the system without human intervention.
Picture of a patient registration form.
Patient entry form to be manually filled
  • Creating EHR document: Electronic healthcare records (EHRs) are the digital version of a patient’s paper chart. OCR is the first step to make the transition between paper and electronic record by capturing relevant fields in the paper chart such as patients’ demographics, progress notes, problems, diagnoses, and medications, and feeding them to an RPA bot which can enter the data to the healthcare record system, process it, and migrate it according to need.
  • Trial matching: OCR can be used to digitize unstructured doctors’ notes and pathology reports, feed them to NLP algorithms used for clinical trial matching (i.e. recognize individuals who would be eligible to participate in a given clinical trial). Then RPA bots can automatically upload the data to the trial master file (TMF) used for managing trial matching and following processes.

What are the challenges that face OCR in RPA?

The goal of automating tasks via RPA bots is to limit human intervention and errors, therefore it is important that the data used to feed the bot is accurate because it will probably not be double checked by a human user. Following is a list of the top challenges of OCR integration with RPA:

  • Page orientation: Wrong page orientation and skewness present a challenge to OCR, therefore, it is important to pair the OCR solution with an image rectification tool in order to de-skew the image and align it properly.
  • Inconsistent text: Many handwritten documents contain scrambled words and poor handwriting. OCR tools may be used in a combination with NLP and ML algorithms to correctly interpret the word based on the general context of the text before feeding it to the RPA system.
  • Glared documents: Several documents such as ID cards and driver’s licenses produce glared images if captured with mobile phone cameras, which increases the probability of OCR mistakes. Some OCR tools integrate light bouncing and blurriness correction to produce better quality scans.

To compare different OCR tools based on data extraction accuracy, feel free to read our data-driven article Best OCR by Text Extraction Accuracy.

Further reading

To learn more about OCR, feel free to read our articles:

And if you believe your business will benefit from an OCR solution, scroll down our data-driven list of OCR vendors.

To explore RPA use cases which rely on OCR, feel free to read our RPA use cases in finance, banking, healthcare, and HR.

If you still have any questions about RPA technology, download our in-depth whitepaper on the topic

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And if you are looking to buy an off-the-shelf RPA solution, consult our comprehensive list of RPA vendors to find the best solution for your business.

And we can guide you to find the right tool that fits your business needs:

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This article was drafted by former AIMultiple industry analyst Alamira Jouman Hajjar.

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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