A system for identifying a defect in a display, the system comprising: a memory; a processor configured to execute instructions stored on the memory, the instructions, when executed by the processor , causing the processor to: receive an input image, wherein the input image comprises an image of a display that shows a test image, wherein the test image is arranged to cause the display to exhibit instances of line Mura; generate a filtered image by preprocessing the input image using at least one filter to remove noise and converge energy towards centre lines; identify a line Mura candidate by: converting the filtered image to a binary image; counting line components marked 1 along a slope in the binary image to count the length of line segments along the slope; and marking a candidate location when a counted number of the line components along the slope exceeds a line threshold; generate an image patch with the candidate location at a center of the image patch; extract a feature vector from the image patch; and classify the image patch as having or not having an instance of line Mura by using a supervised machine learning classifier to which the extracted feature vector is provided. a memory; a processor configured to execute instructions stored on the memory, the instructions, when executed by the processor , causing the processor to: receive an input image, wherein the input image comprises an image of a display that shows a test image, wherein the test image is arranged to cause the display to exhibit instances of line Mura; generate a filtered image by preprocessing the input image using at least one filter to remove noise and converge energy towards centre lines; identify a line Mura candidate by: converting the filtered image to a binary image; counting line components marked 1 along a slope in the binary image to count the length of line segments along the slope; and marking a candidate location when a counted number of the line components along the slope exceeds a line threshold; generate an image patch with the candidate location at a center of the image patch; extract a feature vector from the image patch; and classify the image patch as having or not having an instance of line Mura by using a supervised machine learning classifier to which the extracted feature vector is provided. receive an input image, wherein the input image comprises an image of a display that shows a test image, wherein the test image is arranged to cause the display to exhibit instances of line Mura; generate a filtered image by preprocessing the input image using at least one filter to remove noise and converge energy towards centre lines; identify a line Mura candidate by: converting the filtered image to a binary image; counting line components marked 1 along a slope in the binary image to count the length of line segments along the slope; and marking a candidate location when a counted number of the line components along the slope exceeds a line threshold; converting the filtered image to a binary image; counting line components marked 1 along a slope in the binary image to count the length of line segments along the slope; and marking a candidate location when a counted number of the line components along the slope exceeds a line threshold; generate an image patch with the candidate location at a center of the image patch; extract a feature vector from the image patch; and classify the image patch as having or not having an instance of line Mura by using a supervised machine learning classifier to which the extracted feature vector is provided. <EOS>