How to Solve Captchas when Scraping eCommerce Websites
Tags: captcha, ecommerce, scraping, python
Published: 2024-03-26 08:34:55

![](https://assets.capsolver.com/prod/images/post/2024-03-26/bb83fa75-2ba7-488c-90d1-9d92c38fdf35.png)

When scraping data from eCommerce websites, encountering captchas can be a common challenge. Captchas are used to verify that a user is human and not a bot. For developers using local browsers to scrape data, solving captchas can be a significant obstacle. However, there are third-party solutions available, such as Capsolver, that can help solve captcha challenges through an API integration. In this article, we will explore how to overcome captchas when scraping eCommerce websites.

## Understand Captcha Types when Scraping eCommerce Websites:
Before delving into solutions, it's crucial to understand what Captchas are and why they're employed on eCommerce websites. Captchas are security measures implemented to differentiate between human users and bots. They typically involve tasks like identifying distorted text, selecting images, or solving puzzles. eCommerce websites use Captchas to protect against automated scraping, which can overload servers or scrape sensitive data.

- **Text-based CAPTCHA**,Text-based CAPTCHAs are also a very common form of CAPTCHA, requiring the user to correctly identify and enter a series of characters displayed in a distorted or creative font. The accuracy of the response is then used to decide whether to allow access to the website or not
- **Image-based CAPTCHA**, in image-based CAPTCHAs, the user must recognise and correctly interact with the image to be granted access. These image challenges are visually compelling and proving challenging for automated scripts, as a result of the complex image recognition capabilities they require, which are often outside the capabilities of automated scripts
- **Puzzle-based CAPTCHA**,
Puzzle-based CAPTCHA challenges requiring the user to accurately perform a greater puzzle. This manual verification approach is more secure than text-based CAPTCHAs. Common puzzles include slide puzzles, pattern recognition or colour matching among many other novel recognitions.

## Challenges of Captcha Solving in eCommerce Scraping:
Captchas pose significant challenges to the scraping process. They can slow down scraping operations, leading to delays and reduced efficiency. Manual solving of Captchas is time-consuming and impractical for large-scale scraping tasks. Additionally, traditional captcha-solving methods may not always be accurate or reliable, especially as Captcha designs evolve to combat scraping techniques.

## Strategies for Solving Captchas when Scraping eCommerce Websites:

### Utilize Third-Party Captcha Solving Services:

Capsolver, for example, is a third-party service dedicated to solving Captchas. It offers an API that can be integrated directly into scraping scripts or applications.
By outsourcing Captcha solving to services like Capsolver, you can streamline the scraping process and reduce manual intervention. Sign up for a [free trial](https://dashboard.capsolver.com/dashboard/overview).
Here's an example of the imagetotext that Amazon will encounter, to show the python steps: 

#### Create Task

Create the task with the [createTask](../api-createtask.md).

**Task Object Structure**

Note that this type of task returns the task execution result directly after createTask, rather than getting it
asynchronously through getTaskResult.

| Properties | Type    | Required | Description                                                                                            |
|------------|---------|----------|--------------------------------------------------------------------------------------------------------|
| type       | String  | Required | ImageToTextTask                                                                                        |
| websiteURL | String  | Optional | Page source url to improve accuracy                                                                    |
| body       | String  | Required | base64 encoded content of the image (no newlines) (no data:image/****\*****; base64, content           |
| module     | String  | Optional | Specifies the module. Currently, the supported modules are common and queueit                          |
| score      | Float   | Optional | `0.8 ~ 1`, Identify the matching degree. If the recognition rate is not within the range, no deduction |
| case       | Boolean | Optional | Case sensitive or not                                                                                  |

#### Example Request

```text
POST https://api.capsolver.com/createTask
Host: api.capsolver.com
Content-Type: application/json
```

```json lines
{
  "clientKey": "YOUR_API_KEY",
  "task": {
    "type": "ImageToTextTask",
    "websiteURL": "https://xxxx.com",
    // You can choose the module you need to use
    // ocr single image model, default common
    "module": "queueit",
    // base64 encoded image
    "body": "/9j/4AAQSkZJRgABA......"
  }
}
```

#### Example Response

```json lines
{
  "errorId": 0,
  "errorCode": "",
  "errorDescription": "",
  "status": "ready",
  "solution": {
    "text": "44795sds"
  },
  "taskId": "2376919c-1863-11ec-a012-94e6f7355a0b"
}
```

### Optimize Scraping Parameters:

1. Adjust scraping parameters such as request frequency, user-agent strings, and IP rotation to minimize the occurrence of Captchas.
2. By scraping responsibly and respecting website policies, you can reduce the likelihood of triggering Captchas.


## Conclusion:
Solving Captchas when scraping eCommerce websites is essential for obtaining accurate and reliable data. By employing strategies such as utilizing third-party Captcha-solving services like Capsolver, implementing Captcha-solving algorithms, and optimizing scraping parameters, businesses and researchers can effectively overcome Captchas and extract valuable insights from eCommerce platforms.