SimGAN-Captcha

Uses SimGAN to generate realistic training data from synthetic images, eliminating the need for labor-intensive manual annotation as demonstrated in the HackMIT challenge.

Achieved 95% accuracy on alphanumeric captchas without labeled data, showing the effectiveness of the GAN-based approach in the project's results.

Provides a full workflow from synthetic data generation and GAN refinement to CNN classification, all detailed in the Jupyter notebook.

Serves as a hands-on example of SimGAN architecture and unsupervised learning for computer vision, based on Apple's research paper.