Breaking Text-Based CAPTCHA with Sparse Convolutional Neural Networks

CAPTCHA is an automated test designed to check if the user is human. Though other approaches are explored (such as object recognition), the text-based CAPTCHA is still the main test used by many web service providers, to separate human users from bots. In this paper, a sparse Convolutional Neural Network (CNN) to break textbased CAPTCHA is proposed. Unlike previous CNN solutions, which mainly use fine-tuning and transfer learning from pre-trained models, the proposed framework does not require a pre-trained model. The sparsity constraint deactivates connections between neurons in the CNN fully connected layers that leads to improved accuracy compared to the baseline approach. Visualization of the spatial distribution of neuron activity shed light on the internal learning and the effect of the sparsity constraint.