Paper on dynamics-based motion de-blurring has been published in IEEE TMECH

Michael D. Kim and Jun Ueda, Dynamics-based motion de-blurring improves the performance of optical character recognition during fast scanning of a robotic eye, IEEE Transactions on Mechatronics [Link Early Access]. Abstract: This paper presents a quantitative evaluation of the dynamics-based de-blurring method using an optical character recognition (OCR) technology. Although various image de-blurring algorithms have been studied, there has been no standard performance metric; de-blurred images have often been evaluated in a qualitative manner. In this study, blurry images containing alphanumeric characters were obtained in the course of rapid motion using a robotic vision system. The obtained blurry images were recovered by the dynamics-based de-blurring method. For a quantitative evaluation, OCR rates from the deblurred images by the dynamics-based method were calculated and compared with those by other well-known methods. Experiment results show that the dynamics-based method has the best quantitative results.

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