{"title":"家庭作业整洁度评估基准","authors":"Hanxiao Wu, Zhenyu Zhang, Zhichao Zheng, Fei Shen, Weiwei Zhang, Jianqing Zhu, Huanqiang Zeng","doi":"10.1109/ISPACS48206.2019.8986287","DOIUrl":null,"url":null,"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.","PeriodicalId":6765,"journal":{"name":"2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"53 1","pages":"1-2"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Benchmark for Homework Tidiness Assessment\",\"authors\":\"Hanxiao Wu, Zhenyu Zhang, Zhichao Zheng, Fei Shen, Weiwei Zhang, Jianqing Zhu, Huanqiang Zeng\",\"doi\":\"10.1109/ISPACS48206.2019.8986287\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":6765,\"journal\":{\"name\":\"2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)\",\"volume\":\"53 1\",\"pages\":\"1-2\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISPACS48206.2019.8986287\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPACS48206.2019.8986287","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.
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