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Carbon nanotubes have attracted much attention due to their unique electrical properties, which depend on chirality. Most synthesis techniques produce carbon nanotubes with broad range of chiral angles. A system to identify the different classifications of carbon nanotubes could be very useful, with applications such as automated robotic sorting. An algorithm to extract chirality from images of carbon nanotubes characterized by STM has been developed.