ABSTRACT
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.