Neural networks and artificial intelligence are a burning issue for discussion nowadays. Many experts claim that this technology is not more than just a toy. The practical AI application seems unprofitable to them. We categorically disagree, however. Below, we will dispel myths and talk about entirely real and financially justified applications of artificial intelligence technologies – specifically computer vision in conjunction with screen parsing (screenshots, images etc.).
Businesses often use automated data collection methods to solve their tasks: to monitor competitors, analyze strategies and assortments, verify the work of counterparties etc. In most cases, textual information analysis on web pages is used. However, information may not always be stored in text only. This may be images, video and audio recordings, more complex professional document formats (3D models, CAD system exports etc.). What about them?
What can be "seen" can already be accurately parsed today. All the intricacies and nuances of this process are discussed below.
So, what are screen scrapers and how do they work? Actually, the process of screen parsing (screen scraping) implies the procedure of extracting data from images, screenshots and user interfaces. This method of data extraction is used when simpler and cheaper approaches like text or HTML structure parsing are impossible.
Here is detailed material on what parsing is, its advantages and disadvantages.
It often happens that visual data also contains text and labels, which can be read if you are a person with good eyesight. But for parsing programs that cannot access the data to the page source code, for example, as in the case of a mobile application screen, the task becomes daunting.
In this very case, computer vision and image recognition technologies come to the rescue. They can replace human eyes and "see" what specific information is displayed on the screen (for example, in a screenshot or simply in a photo).
In most cases, scraping serves business needs. Screen scraping is no exception. Here are the most common scenarios for using screen parsers:
There may be more complex situations where screen scraping is applied.
Note! Many software developers, especially those of banking services, government digital services etc. strive to protect information within their products. However, due to the fact that screen scrapers can "see" what is happening on the screen almost like regular users, they can disclose confidential information and sensitive data, intentionally or unintentionally.
For this very reason, one must be extremely cautious. The risk of hacking and financial loss significantly increases. It is important to know and take this fact into account.
A screen scraper turns into a powerful weapon in the hands of malicious users.
When it comes to analyzing plain text or web pages (which are also basically textual documents), the operation of a parser is clear and understandable. The program receives the body of the document, searches for the necessary markup elements or matches with a specified pattern within the text, and then extracts the data inside the element. That's it; the data can be entered into a table and exported in the required format.
A small exception is working with dynamic content, where the end HTML document is generated through JavaScript scripts. In this case, the so-called "headless browsers" are used. They receive the URL to visit, load the page, execute all scripts and only then return the final HTML document to the parser. The work is still completed at the text level.
But how can you screen scrape correctly?
Usually, people only see the final result of the work of most programs. All user interaction takes place in the so-called graphical user interface (GUI).
To know how to screen scrape, you should know that these programs always work with data, not interfaces. Since parsers are also programs, they need to be provided with information in a format that they can "digest".
When using screen scrapers, the readable format would be an image, such as a screenshot of a mobile application screen, or a PDF file, which can be created in a headless browser via API.
Next, optical character recognition (OCR) or computer vision (using specialized neural networks) comes into play.
OCR programs work almost like neural networks. They have a database of images of all known letters, numbers and other characters or symbols. The original image is sliced into suitable pieces (squares or rectangles with contrasting elements in the center), and then each character part is "compressed" to a conditional "standard" and compared with the database of reference images (i.e., with each individual character in the database). As a result, the program selects the character from the database that matches the reference image more than any other.
Computer vision is slightly more complex. The image of characters is processed through a special neural network. Typically, one neural network is configured to work with one language. Certain neurons are trained to detect only a specific character. As a result, the neural network outputs the signal of the neuron that has recognized its letter more accurately than others.
The advantage of a neural network is that it doesn't output disjointed letters and symbols from different alphabets. It immediately forms words. This effect is achieved through additional layers of the neural network, which are trained to look for words rather than characters. Even if one of the characters is incorrectly recognized in the first layer, it will be "corrected" in subsequent layers.
As a result, the screen scraping scheme works as follows:
Instead of neural networks used for text recognition, solutions capable of recognizing and describing objects in the image can be applied. In this case, the recognition module will return to the parser not only the text found on the screen but also object descriptions (like "brown chair" or "scarf of the N brand" etc.).
Basically, we have partially answered the question:
The main distinction lies in the availability of the optical character recognition module (OCR) or computer vision (trained neural network).
This module gives rise to specific advantages and disadvantages of screen scrapers.
Advantages of screen scraping (compared to web parsing):
Here's a guide on how to parse without encountering any blocks.
Disadvantages of screen scraping (compared to web parsing):
The process of web page rendering occurs in a regular or headless browser, which consumes significant resources compared to a classical text parser. Each new thread will require a new browser sample, leading to a rapid increase in resource consumption.
Now that you know what is screen scrape procedure and what it implies, you can initiate and handle the process on your own, taking into account the minor nuances.
No matter how advanced your parser is, even if it can recognize screens and emulate user behavior, you will still need high-quality proxies.
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