A Convolutional Neural Network Based Two-Stage Document Deblurring

Blurring often happens when capturing documents with hand held cameras, which has negative effects on the Optical Character Recognition systems. In this paper, we propose a Convolutional Neural Network (CNN) based two-stage deblurring method. The method can deal with both real motion blur and focal blur situations, while it does not require exact estimation of the blur kernel. To achieve this, the whole blur kernel space is divided into several degradative sub-spaces. Firstly, a CNN classifier is trained to predict which sub-space the blurry image belongs to at the patch level. Then, several patches voting for the specific blur kernel sub-space is developed. Given the strong learning ability of CNN, only one CNN model corresponding to a degradative kernel sub-space is trained to restore the sharp images in the image restoration step. Experimental results show that the proposed approach performs well on the real blurring document images. In addition, we demonstrate that the proposed method could also handle the spatially-varying blurring