Automatic blur detection in mobile captured document images: Towards quality check in mobile based document imaging applications

Optical Character Recognition is widely used for automated processing of document images. While character recognition technology is mature, its application to mobile captured document image is still at its nascent stage. Capturing images from a mobile camera poses several challenges like motion blur, defocus and geometrical distortions which are usually not encountered in scanned or calibrated camera captured images. Therefore determining the quality of images automatically prior to recognition is an important problem. Quality check is especially useful in financial transaction instruments like bill payment where accuracy of text recognition for sensitive fields such as “amount due” should be high. Poor quality images can be rejected prior to OCR to avoid incorrect text recognition and save processing time. This paper discusses some techniques in literature for blur detection in mobile camera captured document images. We propose a simple yet elegant method that addresses some challenges faced in these document images. Extensive testing is performed on large dataset containing more than 4000 mobile captured images and optimum parameter values for performing quality check against motion blur and defocus are identified. Our experimental results demonstrate the effectiveness of the proposed method. In addition we realized a smart mobile application for blur detection and report its performance on several mobile devices.