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Homework grading is critical to evaluate teaching quality and effect.However, it is usually time-consuming to grade the homework manually. Inautomatic homework grading scenario, many optical mark reader (OMR)-basedsolutions which require specific equipments have been proposed. Although manyof them can achieve relatively high accuracy, they are less convenient forusers. In contrast, with the popularity of smart phones, the automatic gradingsystem which depends on the image photographed by phones becomes moreavailable. In practice, due to different photographing angles or uneven papers,images may be distorted. Moreover, most of images are photographed undercomplex backgrounds, making answer areas detection more difficult. To solvethese problems, we propose BAGS, an automatic homework grading system which caneffectively locate and recognize handwritten answers. In BAGS, all the answerswould be written above the answer area underlines (AAU), and we use twosegmentation networks based on DeepLabv3+ to locate the answer areas. Then, weuse the characters recognition part to recognize students' answers. Finally,the grading part is designed for the comparison between the recognized answersand the standard ones. In our test, BAGS correctly locates and recognizes thehandwritten answers in 91% of total answer areas.

BAGS: An automatic homework grading system using the pictures taken by smart phones