Overview
Our client needed to scrape data from a specific portal that required a CAPTCHA to be solved during login. The CAPTCHA was challenging, often requiring human intervention. The client sought a solution to automate this process and reduce human involvement. We addressed this by implementing a vision-based model to solve the CAPTCHA automatically.
Problem
The primary challenge was the CAPTCHA presented during the login process, which was difficult even for humans to solve on the first try. This led to frequent interruptions as the bot had to stop and wait for a human to solve the CAPTCHA via Slack. This process was inefficient and slowed down the data scraping operations significantly.
Solution
To address the problem, we developed and trained a vision-based model to automatically solve the CAPTCHA with high accuracy. Here’s how we approached the solution:
- Data Collection and Preprocessing: We collected a substantial dataset of CAPTCHA images from the portal and labeled them accordingly.
- Model Training: We trained a vision-based model using this dataset, ensuring it learned to recognize and solve the CAPTCHA images accurately.
- Integration with Scraping Bot: The trained model was integrated into the scraping bot. When the CAPTCHA appeared, the bot would capture the image, pass it to the model, and use the predicted solution to proceed with the login.
- Fallback Mechanism: In case of any failure (e.g., if the CAPTCHA solution was incorrect), a fallback mechanism was set up where the bot would send the CAPTCHA image to a Slack channel for human resolution.
Results
The implementation of the vision-based model resulted in significant improvements:
- Accuracy: The model achieved a 98% accuracy rate in solving the CAPTCHA.
- Efficiency: The need for human intervention was reduced drastically, with the bot handling the majority of CAPTCHA challenges automatically.
- Time Savings: The time taken to solve CAPTCHA challenges and resume data scraping was reduced from several minutes to mere seconds in most cases.
- Operational Continuity: The data scraping process became more continuous and less prone to interruptions, enhancing overall efficiency.
Conclusion
By leveraging machine learning, we successfully automated the CAPTCHA resolution process for our client’s data scraping needs. The vision-based model not only improved the accuracy and efficiency of the login process but also significantly reduced the dependency on human intervention. This case demonstrates the potential of AI in overcoming complex challenges and streamlining operations in data-driven tasks.