How to Solve CAPTCHAs While Web Scraping in 2024
As a web scraping expert, one of the most common and frustrating obstacles you‘ll come across are CAPTCHAs. Short for "Completely Automated Public Turing test to tell Computers and Humans Apart", CAPTCHAs are challenge-response tests designed to determine whether a user is human or an automated bot.
While CAPTCHAs play an important role in preventing spam and protecting websites from malicious bots, they can be a major headache for web scrapers. Encountering a CAPTCHA can bring your web scraping project to a grinding halt.
In this in-depth guide, we‘ll cover everything you need to know about solving CAPTCHAs while web scraping in 2024. You‘ll learn about the different types of CAPTCHAs, CAPTCHA solving techniques and services, best practices for avoiding CAPTCHAs, and more.
Why Websites Use CAPTCHAs
CAPTCHAs are one of the most common security measures implemented by websites to filter out bots and automated traffic. The purpose is to verify that a real human is interacting with the site, not a bot performing automated actions.
Websites use CAPTCHAs for a variety of reasons:
- Preventing spam in comments, contact forms, reviews, etc.
- Protecting limited resources like event ticket sales from purchase by scalper bots
- Stopping hackers from using automated tools to brute-force passwords
- Ensuring only humans can create accounts to maintain integrity of the service
- Limiting bot traffic to reduce server load and bandwidth costs
As a web scraper, your automated bot will often trigger CAPTCHAs, especially if you are making a high volume of requests. Websites see this as suspicious bot-like behavior.
The Many Types of CAPTCHAs
Over the years, CAPTCHAs have evolved and become more sophisticated. Early CAPTCHAs simply displayed distorted text that a user had to decipher and retype. But these were too easy for bots using optical character recognition (OCR) to solve.
Here are some of the most common types of CAPTCHAs used by websites today:
Text-based: Asks user to retype distorted, overlapping, or discolored letters and numbers.
Image recognition: Presents a grid of images and asks the user to select the images matching a description, like "select all images with traffic lights". Google‘s reCAPTCHA v2 is a popular example.
Rotate/orientation: Shows an image that is rotated and asks the user to rotate it to the correct orientation, like turning an object right-side up.
Puzzle: Requires dragging and dropping a puzzle piece into the correct location in an image. May also involve reordering image pieces.
Math problems: Presents simple math problems that the user must solve.
Click a button: Simply requires checking a box next to "I am not a robot". reCAPTCHA v3 uses this but evaluates the user‘s interaction with the page to generate a score of how likely they are to be human.
Audio: An audio recording reads letters or numbers aloud, which the user must enter. Useful as an accessibility alternative to visual CAPTCHAs.
The most secure CAPTCHAs do not rely on a single test but combine different types into a multi-step challenge. For example, requiring the user to click the correct images and type the numbers they contain.
Knowing what type of CAPTCHA you are dealing with is the first step to figuring out how to solve it. No one technique works for every type of CAPTCHA so you need to identify the CAPTCHA to determine your approach.
CAPTCHA Integration Solutions
Many websites use CAPTCHA services rather than developing their own. These allow webmasters to easily add CAPTCHA capabilities by inserting a few lines of code.
Google‘s reCAPTCHA is by far the most popular, being used by over 5 million websites. reCAPTCHA comes in three versions:
reCAPTCHA v3 analyzes user interactions to provide a score of how likely a user is to be human. It is invisible to the user and requires no solving.
reCAPTCHA v2 presents a checkbox saying "I‘m not a robot" and evaluates mouse movements and cookies to verify the user. If considered suspicious, an image recognition challenge is shown.
reCAPTCHA v1 (no longer in use) displayed distorted text for users to retype.
Other CAPTCHA solutions include hCaptcha, Arkose Labs, and ConfirmU. hCaptcha is unique in that it not only protects websites, but the CAPTCHA solving data is used to train machine learning algorithms. Arkose Labs offers an innovative "enforcement challenge" that presents interactive puzzles.
How to Solve CAPTCHAs
Now that you understand the different types of CAPTCHAs, let‘s look at the methods you can use to solve them.
- Human-Based CAPTCHA Solving Services
The most reliable way to solve CAPTCHAs is using human-based CAPTCHA solving services. You submit the CAPTCHA images to these services which have large teams of human employees who manually solve the CAPTCHAs.
Popular human CAPTCHA solving services include:
- 2Captcha
- DeathByCaptcha
- Anti-Captcha
- ImageTyperz
- CapMonster Cloud
These services offer APIs that allow you to submit CAPTCHA challenges programmatically. You typically sign up for an account, pre-pay a balance, then the cost per solved CAPTCHA is deducted from your balance.
Prices range from $0.50-$2.00 per 1000 CAPTCHAs solved. Most services offer a free trial so you can see if they meet your needs before committing funds. Look for services with high solving accuracy and 24/7 support.
To integrate these into your scraping process:
Detect when a CAPTCHA is encountered. Look for page elements like a reCAPTCHA URL or specific
<div>IDs.Take a screenshot of the CAPTCHA element or download the image if it‘s a URL.
Submit the CAPTCHA to the solving service via their API, using your programming language‘s HTTP library.
Retrieve the answer from the API response. Answers are typically returned in under 30 seconds.
Input the solution into the CAPTCHA field and submit the form to proceed with your scraping.
It‘s a fairly straightforward process to outsource CAPTCHA solving this way. The main drawbacks are the added cost and potential for delays. But if you need close to 100% solving accuracy, this is the way to go.
- CAPTCHA Solving with OCR and Machine Learning
Rather than relying on humans, you can attempt to solve CAPTCHAs programmatically using optical character recognition (OCR) and machine learning. This approach works best on simple text-based CAPTCHAs.
OCR libraries like Tesseract can recognize and extract text from images. By preprocessing the CAPTCHA image to remove noise and distortion, you can often get OCR to detect the CAPTCHA text.
However, modern text CAPTCHAs are often too visually distorted and complex for basic OCR to solve. Trying to remove the lines, warp, and discoloration from the text is extremely difficult.
Instead, machine learning models can be trained on large datasets of CAPTCHA images to "learn" how to decipher the distorted text. By feeding the model thousands of CAPTCHAs and their solutions, it can learn the visual patterns to predict the text of new CAPTCHAs.
To train your own CAPTCHA solving model, you need:
A large labeled dataset of CAPTCHAs including the images and correct text solutions. You may be able to find public datasets or generate your own CAPTCHAs.
Select a machine learning model architecture like a convolutional neural network that performs well on image classification tasks.
Split your CAPTCHA dataset into training and testing sets. Feed batches of images into the model and backpropogate the error to improve the model‘s predictions.
Evaluate the trained model‘s performance on the test set. Aim for an accuracy of 90%+ before using it for real CAPTCHAs.
Be aware that this is a very challenging task that requires significant expertise in machine learning and hundreds of hours of compute time to train models. Pre-trained CAPTCHA solving models exist, but their accuracy varies and most only work on a limited set of CAPTCHA styles.
With image recognition and behavioral analysis CAPTCHAs, machine learning is even less effective. Farms of "click workers" are used to generate training data for these, so ML models have not surpassed humans in accuracy. It‘s an ongoing arms race between CAPTCHA developers and bot makers.
- Manually Solving CAPTCHAs
If you only encounter CAPTCHAs occasionally, it may be simplest to just solve them manually yourself. You can automate 90% of your scraping workflow and just complete the CAPTCHAs as they pop up.
When a CAPTCHA is detected, have your script pause and alert you with a notification sound. Solve the CAPTCHA yourself, then click a button to tell the script to resume.
This is only viable for small scraping projects. But for personal data collection, it doesn‘t make sense to pay a solving service or mess with ML if you only need to solve a CAPTCHA every 10 minutes or so.
Tools for No-Code CAPTCHA Solving
No-code web scraping tools aim to make data extraction accessible to non-programmers. You select site elements to scrape visually, without needing to inspect source code or write scripts.
But this drag-and-drop approach doesn‘t work well with CAPTCHAs, which are explicitly designed to block bots. There‘s no easy way to automate solving CAPTCHAs without code.
However, no-code tool Octoparse does offer some ways to semi-automate CAPTCHA solving:
Insert "wait" actions if a CAPTCHA element is detected. This pauses the scraper so you can solve the CAPTCHA manually before resuming.
On Octoparse Cloud, you can request they customize the scraping job to use a CAPTCHA solving service API when CAPTCHAs appear.
For reCAPTCHA v3, which requires no user interaction, Octoparse may be able to generate a valid token to bypass the CAPTCHA. But this is hit-or-miss.
Ultimately, no-code tools are limited in their abilities to handle CAPTCHAs. For reliable CAPTCHA solving, you‘re better off building your own scraper or using a dedicated CAPTCHA solving service.
Tips to Avoid CAPTCHAs
Ideally, you want to reduce the number of CAPTCHAs you encounter while scraping from the start. By making your scraper appear more human-like, you can avoid many bot detection triggers.
Here are some tips to prevent CAPTCHAs from interrupting your scraping:
Use IP rotation and proxies: CAPTCHAs often trigger if they see an unusually high number of page requests coming from a single IP address in a short time period. By rotating your IP on each request using proxies, you distribute your traffic to appear as multiple users.
Spoof your user agent: The user agent is a string browsers send to identify themselves to web servers. A mismatch between your user agent and actual browser can signal a bot. Send a user agent string consistent with a real browser that matches your scraping client.
Randomize access patterns: Bots tend to access pages in a predictable pattern, like by incrementing IDs in the URL. Randomizing the order you visit URLs and adding variable delays between requests makes you look more human.
Respect robots.txt: The robots.txt file specifies which pages on a site are allowed to be scraped. Googlebot and other "good bots" respect these rules. Consider obeying robots.txt to avoid looking like a bad bot.
Avoid honeypot traps: Some sites include hidden links that are invisible to regular users. But bots find these links in the page source and follow them. If your scraper hits these honeypots, it signals you are a bot. Use your scraper to only find links visible in the rendered page.
Use a headless browser: Tools like Puppeteer and Selenium control real browsers like Chrome. By loading pages in an actual browser, your requests look the same as an actual user session. Headless browsers run the browser without displaying the GUI window.
Being an ethical web scraper means treading lightly and only scraping publicly available data you have permission for. Hammering a site to get around its bot protection measures not only triggers CAPTCHAs but may get your IP blocked or even prompt legal action.
The Future of CAPTCHAs
With advancements in machine learning and computer vision, CAPTCHAs have had to become increasingly difficult for machines to solve. While early CAPTCHAs could be defeated with simple image processing and OCR, that‘s no longer the case.
The most robust CAPTCHA solutions today use multi-modal challenges that combine text, images, and behavioral analysis. Google‘s reCAPTCHA v3 collects data points on a user‘s click locations, cursor movements, and time spent on the page to inform its bot detection algorithms.
Additionally, CAPTCHAs are moving toward a behind-the-scenes approach rather than directly interrupting users to solve tests. By collecting behavioral data like typing speed, scroll patterns, and browser fingerprints, these "frictionless" CAPTCHAs can identify bots more seamlessly.
As long as bots remain a problem, CAPTCHAs will continue to evolve to weed them out. However, the balance between strong security and user friction is a delicate one. While CAPTCHAs are necessary, users will quickly get fed up if they are too frequent or difficult.
More websites may shift to using "proof of work" challenges that require bots to compute some resource-intensive function to get access. This imposes a cost on bot requests to disincentivize abuse. But this also costs web servers CPU load to verify.
No matter what direction CAPTCHAs go, web scrapers will need to adapt their techniques to keep up. Using human-powered solving services and mimicking human behavior patterns will remain essential to getting around bot detection.
Ultimately, web scraping is a constant game of cat-and-mouse. As a scraper, you must strive to make your bot indistinguishable from human users to reliably extract the data you need.