How cool tools simplify parsing complex data

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The Internet today is tens of billions of pages. Information is updated daily. According to Statista, from 2013 to 2023, the amount of data on the Internet has grown almost 20 times - from 4 zettabytes to 79 zettabytes.

The ways of presenting it have also become more complex: dynamic pages, JavaScript content, and data embedded in images. This cuts off standard parsing methods like Python scripts with BeautifulSoup or Scrapy. They still work well for extracting text from HTML. But if a site uses dynamic elements, captchas, or protects the API from bulk requests, such tools break down.

This is where advanced tools come to the rescue. They learn from complex data, recognize text from images, adapt to changes in the structure of websites, and find patterns in information flows.

In this article, we will show you how to collect hard-to-access data using AI.

parsing

How to use AI in parsing?

Here's a step-by-step guide with specific tools.

Recognize data from images and complex formats

If you want to extract text from images, PDFs or scans, start with OCR (Optical Character Recognition). One of the best tools for this is Tesseract.

For this:

If you have Python, you usually have Tesseract as well, but you can install it just in case. At the Windows Command Prompt, type:

pip install pytesseract

Tesseract

Now prepare the image you want to process.

Recognize data

Write a script to recognize text. In our example, the file is on disk D. You will specify your path:

from PIL import Image

import pytesseract

# Open the image

image = Image.open('D:\\Folder\\example.png') # Specify the path to the file

# Recognize text

text = pytesseract.image_to_string(image)

print(text)

Note:

On Windows, use double backslashes (\\) to avoid escaping errors.
Replace D:\\Folder\\example.png with the path to your file.

What if I don't want to write code?

If you work with large files, such as large PDF documents or arrays of images, and don't want to bother with programming, there are ready-made tools available. For example, Adobe OCR for optical text recognition in complex documents.

Advantages of Adobe OCR

Processes multiple pages or entire archives without loss of quality.
Recognizes text even on images with low quality or complex layout.
If you have an Acrobat subscription, you can use the built-in OCR features without additional customizations.

How to use Adobe OCR

Open a document in Adobe Acrobat.
Go to Tools → Scan & OCR.

Adobe OCR

Select a file or area to process.
Click Recognize Text to extract text from an image or PDF.
Save the result in a convenient format (TXT, Word, Excel).

Adobe OCR is particularly useful for recognizing large amounts of data, such as processing scanned contracts, reports or archives.

You can also use a tool from Google.

If you need to not only extract text, but also identify objects in an image or analyze a document, try Google Vision API. This is a cloud-based solution from Google that is suitable for both simple and complex tasks.

What Google Vision API can do

Recognize text (OCR) with high accuracy.
Identify objects, logos, and even emotions in photos.
Analyze documents: highlight headings, tables, graphs.

How to get started?

The service provides a convenient interface for testing.

Go to the official Google Vision API page.
Upload an image in the interface.
Select the required type of analysis (for example, OCR or object search).
Get the result in the form of text or report.

Cost

Google Vision API is a paid service where each iteration of testing costs $0.35. However, for new users, Google provides $300 for free testing. This allows you to process hundreds of images before asking for payment.

Google Vision API is suitable if you need to quickly process data, retrieve text, and analyze an image. The service is useful for getting started with AI tools without deep technical knowledge.

Working with dynamic pages

If a site uses JavaScript to render content, standard parsers like BeautifulSoup are powerless. This is where browser emulation tools like Puppeteer or Playwright can help.

How to work with Playwright

Install the library:

pip install playwright

playwright install

Write a script to retrieve the data:
from playwright.sync_api import sync_playwright

with sync_playwright() as p:

browser = p.chromium.launch(headless=True)

page = browser.new_page()

page.goto("https://example.com")

# Waiting for JavaScript to load

page.wait_for_selector('h1')

# Retrieving the data

data = page.inner_text('h1')

print(data)

browser.close()

Playwright automatically executes JavaScript and displays the page as the user sees it.

Tips:

Customize delays (sleep) to avoid bans.
Use proxies to distribute search queries on the site.

Processing of large amounts of data

After collecting the information, the data should be structured and analyzed. For example, if you have collected product reviews, you can automatically categorize them with spaCy.

How to get started with spaCy

Install spaCy:

pip install spacy

python -m spacy download en_core_web_sm

Write code to analyze the text:
import spacy

nlp = spacy.load("en_core_web_sm")

text = "The product is amazing, but the delivery was slow."

doc = nlp(text)

for ent in doc.ents:

print(ent.text, ent.label_)

SpaCy allows you to extract names, dates, key phrases and categorize text.

Tips:

Use pre-trained models to avoid wasting time on customization.
For Russian-language data, the Natasha library is suitable.

Bypassing parsing protections

2Captcha

For bypassing captchas and anti-bot systems will be useful:

2Captcha or Anti-Captcha for solving captchas.
Anti-detect browsers like Multilogin to simulate a real user.

How to work with 2Captcha

Install the library:

pip install python-anticaptcha

Solve the captcha:

from anticaptchaofficial.recaptchav2proxyless import *

solver = recaptchav2proxyless()

solver.set_verbose(1)

solver.set_key("YOUR_API_KEY")

solver.set_website_url("https://example.com")

solver.set_website_key("SITE_KEY")

response = solver.solve_and_return_solution()

if response != 0:

print("Result: ", response)

else:

print("Error: ", solver.error_code)

These tools will help to parse the data.

How to connect proxies when using AI

Example of using requests with proxy to work with API:

import requests

url = "https://api.openai.com/v1/completions"

headers = {

"Authorization": "Bearer YOUR_API_KEY"

}

data = {

"prompt": "Explain AI-based parsing",

"max_tokens": 50

}

proxies = {

"http": "http://proxy_address:port",

"https": "http://proxy_address:port"

}

response = requests.post(url, json=data, headers=headers, proxies=proxies)

print(response.json())

Add proxies to dynamic parsers

Puppeteer or Playwright are used to work with dynamic pages and analyze data. Proxies are configured in the same way as for normal parsing.

Example for Playwright:

from playwright.sync_api import sync_playwright

with sync_playwright() as p:

browser = p.chromium.launch(

headless=True,

proxy={"server": "http://proxy_address:port"}

)