Edge-Preserving Camera Trajectories for Improved Optical Character Recognition on Static Scenes with Text

Abstract

Camera systems in fast motion suffer from the effects of motion blur, which degrades image quality and can have a large impact on the performance of visual tasks. The degradation in image quality can be mitigated through the use of image reconstruction. Blur effects and the resulting reconstruction performance are highly dependent on the point-spread function resulting from camera motion. This work focuses on the motion planning problem for a camera system with boundary conditions on time and position, with the objective of improving the performance of optical character recognition. Tuned edge-preserving trajectories are shown to result in higher recognition accuracy when compared to inverse error and linear trajectories. Simulation and experimental results provide quantitative measures to verify edge-preservation and greater recognition performance.