Text Deblurring Using OCR Word Confidence

Objective of this paper is to propose a new Deblurring method for motion blurred textual images. This technique is based on estimating the blur kernel or the Point Spread Function of the motion blur using Blind Deconvolution method. Motion blur is either due to the movement of the camera or the object at the time of image capture. The point spread function of the motion blur is governed by two parameters length of the motion and the angle of the motion. In this approach we have estimated point spread function for the motion blur iteratively for different values of the length and angle of motion. For every estimated PSF we perform the Deconvolution operation with the blurred image to get the nonblurred or the latent image. Latent image obtained is then feed to an Optical character recognition so that the text in that image can be recognized. Then we calculate the Average Word Confidence for the recognized text. Thus for every estimated Point Spread Function and the obtained latent image we get the value of Average Word Confidence. The Point Spread Function with the highest Average Word Confidence value is the optimal Point Spread Function which can be used to deblur the given textual image. In this method we do not have any prior information about the PSF and only single image is used as an input to the system. This method has been tested with the naturally blurred image taken manually and through the internet as well as artificially blurred image for the evaluation of the results. The implementation of the proposed algorithm has been done in MATLAB.