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

arXiv: https://arxiv.org/abs/1906.03767 · HTML: https://mlsanity.com/paper/1906.03767/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [VEHME: A Vision-Language Model For Evaluating Handwritten Mathematics Expressions](https://mlsanity.com/paper/2510.22798/) (2025)
- [Teaching LLMs for Step-Level Automatic Math Correction via Reinforcement Learning](https://mlsanity.com/paper/2503.18432/) (2025)

## Abstract (arXiv)

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.