Name
Abstract | ||
|---|---|---|
The homework tidiness assessment aims to auto evaluate the writing tidiness of homework, playing an important role in daily teaching. However, there is still no comprehensive basis for homework tidiness assessment. For this, a benchmark for homework tidiness assessment (HTA) is proposed. Firstly, a database named HTA 1.0 containing 1000 homework images is collected. Each image is manually annotated by multiple volunteers. Secondly, a comprehensive evaluation protocol is designed, using mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and accuracy (Acc) as performance indicators. Finally, three deep learning models (i.e., LeNet, AlexNet and VGGNet) are applied as baseline methods and the results are reported and analyzed. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ISPACS48206.2019.8986287 | 2019 INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING AND COMMUNICATION SYSTEMS (ISPACS) |
Keywords | Field | DocType |
Homework Tidiness Assessment, Deep Learning, Benchmark | Mean absolute percentage error,Computer vision,Performance indicator,Computer science,Mean absolute error,Mean squared error,Artificial intelligence,Deep learning,Statistics | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hanxiao Wu | 1 | 0 | 0.34 |
| Zhenyu Zhang | 2 | 0 | 0.34 |
| Zhichao Zheng | 3 | 0 | 0.34 |
| Fei Shen | 4 | 0 | 0.34 |
| Weiwei Zhang | 5 | 0 | 0.34 |
| Jianqing Zhu | 6 | 0 | 2.03 |
| Huanqiang Zeng | 7 | 395 | 36.94 |