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