Automated image quality assessment for camera-captured OCR

Camera-captured optical character recognition (OCR) is a challenging area because of artifacts introduced during image acquisition with consumer-domain hand-held and Smart phone cameras. Critical information is lost if the user does not get immediate feedback on whether the acquired image meets the quality requirements for OCR. To avoid such information loss, we propose a novel automated image quality assessment method that predicts the degree of degradation on OCR. Unlike other image quality assessment algorithms which only deal with blurring, the proposed method quantifies image quality degradation across several artifacts and accurately predicts the impact on OCR error rate. We present evaluation results on a set of machine-printed document images which have been captured using digital cameras with different degradations.