In this paper, we propose a novel method for rotation invariant pattern recognition. We perform adaptive denoising to the input pattern images. If the noise level is above a threshold, we perform block matching and 3D filtering (BM3D) to reduce noise from the noisy pattern images. We do not conduct denoising otherwise. We extract ridgelet-Fourier features from the denoised pattern and classify the unknown pattern to one of the known classes with the nearest neighbor classifier. Experiments demonstrate that our new method achieves perfect classification rate (100%) for two datasets and different noise levels and different rotation angles, and it outperforms several existing methods for invariant pattern recognition.