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Underlying Technology of RPA & Web Scrapers: Screen Scraping

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Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is 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 focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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Screen scraping is the cornerstone of emerging technologies such as RPA and web scrapers which rely on it to extract data from digital screens. Early screen scraping applications were used to extract source codes from legacy systems and migrate them to modern applications. However, modern technologies, such as OCR and computer vision, enable screen scraping to pull targeted data from any running application.

In this article, we explore what screen scraping is, how it works, business applications, and challenges.

What is screen scraping?

Screen scraping, previously known as terminal emulation, is the process of automatically collecting visual data from digital screens and entering it into different applications or systems on the device without human intervention to re-key the data. Screen scraping was originally used to transfer data from legacy applications running on mainframes (e.g. IBM mainframe) and display it onto modern PC applications (e.g. excel).

Why is screen scraping important to businesses?

Screen scraping enables users to automate rule-based, repetitive data transfer processes, providing benefits such as:

  • Ensure data quality: Relying on scripts to extract and transfer data without human intervention reduces errors (e.g. duplicates, typos, missing data).
  • Reduce data processing time: Screen scraping applications reduce the time of manual data collection and transformation. UiPath, a screen scraping and RPA provider, claims that screen scraping can achieve 100% accuracy in data collection from different applications in ~16ms.

How does screen scraping work?

Screen scrapers are programs scripted to:

  • search for and identify specific UI elements predetermined by the user
  • extract data from designated UI elements (e.g. columns in spreadsheets, buttons on websites)
  • transfer extracted data into designated applications
    • in case of unstructured data (e.g. images, PDFs), the screen scraper will leverage OCR to transform the data into machine-readable text before entering it to the designated application.

Where can businesses implement screen scraping?

There are 2 main applications of screen scraping in business:

App to App – RPA

RPA is one of the most important technologies in digital transformation as it enables the automation of numerous repetitive UI-dependent processes, such as daily P&L preparation in finance, updating inventory records in the supply chain, or entering patient data into electronic health records (EHR) in healthcare.

RPA bots leverage screen scraping to replicate users’ interactions with UI elements in order to achieve a specific process. For example, an RPA bot relies directly on screen scraping to be able to:

  1. Log into a user’s email
  2. Click on invoice-related emails (the bot will be programmed to recognize relevant keywords)
  3. Download attachment
  4. Open attachment from downloads file (e.g. PDF or image)
  5. Search for payment amount
  6. Transfer payment data from the PDF or image file to a designated spreadsheet

Web to App – Web Scraping

Web scrapers are a type of software that automate real-time data extraction from online resources and send it to users in designated machine-readable formats (e.g. JSON, CSV). Industries such as e-commerce, finance, and real estate rely on web scraping for:

Web scrapers rely directly on screen scraping to detect HTTP elements and transfer the data into the designated format.

To understand the differences between web scraping and screen scraping, see our in-depth guide to web scraping vs. screen scraping: techniques & applications.


The following video demonstrates how businesses use Bright Data’s Data Collector uses screen scraping to pull e-commerce data such as product features, price, and image in real time, transform it into structured data, and send it to users:

Nonetheless, web scraping faces challenges as businesses try to create barriers to scraping bots to mitigate website traffic overload and ensure the privacy of their content. These barriers can be in the form of:

  • Robot.txt
  • IP blockers
  • Honeypots

To explore how businesses tackle these challenges, read our in-depth article about web scraping challenges.

Most websites employ anti-bot measures, such as analyzing the timing and pattern of requests, to detect bot-like behavior. If a website detects that you are sending non-human traffic, it may block your IP address to prevent you from accessing the content. 

Web Unblocker is an AI-powered proxy solution that allows users to circumvent anti-bot systems by helping scrapers imitate real website users. Oxylabs’ Web Unblocker uses auto-retry functionality, dynamic browser fingerprinting, ML-driven proxy management, and response recognition technologies to automate the screen scraping process.

There are numerous technologies today similar to screen scraping enabling machines to understand unstructured data. To explore such technologies, read our in-depth articles:

And if you believe your business will benefit from web scraping and automation solutions, check out our data-driven lists of providers:

And we can help you choose the right product for your business:

Find the Right Vendors

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 is 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 focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology 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.

Sources: Traffic Analytics, Ranking & Audience, Similarweb.
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Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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