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I made a simple ruby script that implements the method described above.

I made a simple ruby script that implements the method described above.

• for each timer region found during the scanning process, crop all scanned frames to the timer region
• group these images by similarity (squared difference), and select only the ~10-15 groups with the highest frequency, these are most likely the actual digits
• average all images of each group to get an idealized sample for each digit

• for each timer region found during the scanning process, crop all scanned frames to the timer region
• group these images by similarity (squared difference), and select only the ~10-15 groups with the highest frequency, these are most likely the actual digits
• average all images of each group to get an idealized sample for each digit
• if the timer does is not rectangular (eg. italic text), save the original mask for the timer region and compare only these pixels

(left: all images from intervals classified as containing the timer extracted from the timer area; right: images grouped by sum of squared difference)