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How Python Developers Can Fix Warped Phone Photos in OMR Grading Systems

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Optical Mark Recognition (OMR) grading systems, widely used to evaluate multiple-choice exams, face accuracy problems when processing photos taken on smartphones. Geometric distortions such as perspective warp, tilted angles, and uneven lighting can cause coordinate-based systems to misread answer bubbles. Unlike clean scanned documents, phone images shift fixed pixel coordinates enough to produce incorrect grading results. Developers can address this by applying OpenCV perspective transforms, object detection models like YOLO, or image preprocessing techniques such as thresholding and edge detection. Combining multiple approaches is recommended for the most reliable outcome.

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