Improving automatic text recognition through atmospheric turbulence

Image quality degradation caused by atmospheric turbulence reduces the performance of automated tasks such as optical character recognition. This issue is addressed by fine-tuning text recognition models using turbulencedegraded images. As obtaining a realistic training dataset of turbulence-degraded recordings is challenging, two synthetic datasets were created: one using a physics-inspired deep learning turbulence simulator and one using a heat chamber. The fine-tuned text recognition model leads to improved performance on a validation dataset of turbulence-distorted recordings. A number of architectural modifications to the text recognition model are proposed that allow for using a sequence of frames instead of just a single frame, while still using the pre-trained weights. These modifications are shown to lead to a further performance improvement.