Focal CTC Loss for Chinese Optical Character Recognition on Unbalanced Datasets
In this paper, we propose a novel deep model for unbalanced distribution Character Recognition by employing focal loss based connectionist temporal classification (CTC) function. Previous works utilize Traditional CTC to compute prediction losses. However, some datasets may consist of extremely unba...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/9345861 |
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| _version_ | 1849404419749183488 |
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| author | Xinjie Feng Hongxun Yao Shengping Zhang |
| author_facet | Xinjie Feng Hongxun Yao Shengping Zhang |
| author_sort | Xinjie Feng |
| collection | DOAJ |
| description | In this paper, we propose a novel deep model for unbalanced distribution Character Recognition by employing focal loss based connectionist temporal classification (CTC) function. Previous works utilize Traditional CTC to compute prediction losses. However, some datasets may consist of extremely unbalanced samples, such as Chinese. In other words, both training and testing sets contain large amounts of low-frequent samples. The low-frequent samples have very limited influence on the model during training. To solve this issue, we modify the traditional CTC by fusing focal loss with it and thus make the model attend to the low-frequent samples at training stage. In order to demonstrate the advantage of the proposed method, we conduct experiments on two types of datasets: synthetic and real image sequence datasets. The results on both datasets demonstrate that the proposed focal CTC loss function achieves desired performance on unbalanced datasets. Specifically, our method outperforms traditional CTC by 3 to 9 percentages in accuracy on average. |
| format | Article |
| id | doaj-art-8fc834f8bc5c4bb5ab5c93873dee3364 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-8fc834f8bc5c4bb5ab5c93873dee33642025-08-20T03:36:59ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/93458619345861Focal CTC Loss for Chinese Optical Character Recognition on Unbalanced DatasetsXinjie Feng0Hongxun Yao1Shengping Zhang2Harbin Institute of Technology, ChinaHarbin Institute of Technology, ChinaHarbin Institute of Technology, ChinaIn this paper, we propose a novel deep model for unbalanced distribution Character Recognition by employing focal loss based connectionist temporal classification (CTC) function. Previous works utilize Traditional CTC to compute prediction losses. However, some datasets may consist of extremely unbalanced samples, such as Chinese. In other words, both training and testing sets contain large amounts of low-frequent samples. The low-frequent samples have very limited influence on the model during training. To solve this issue, we modify the traditional CTC by fusing focal loss with it and thus make the model attend to the low-frequent samples at training stage. In order to demonstrate the advantage of the proposed method, we conduct experiments on two types of datasets: synthetic and real image sequence datasets. The results on both datasets demonstrate that the proposed focal CTC loss function achieves desired performance on unbalanced datasets. Specifically, our method outperforms traditional CTC by 3 to 9 percentages in accuracy on average.http://dx.doi.org/10.1155/2019/9345861 |
| spellingShingle | Xinjie Feng Hongxun Yao Shengping Zhang Focal CTC Loss for Chinese Optical Character Recognition on Unbalanced Datasets Complexity |
| title | Focal CTC Loss for Chinese Optical Character Recognition on Unbalanced Datasets |
| title_full | Focal CTC Loss for Chinese Optical Character Recognition on Unbalanced Datasets |
| title_fullStr | Focal CTC Loss for Chinese Optical Character Recognition on Unbalanced Datasets |
| title_full_unstemmed | Focal CTC Loss for Chinese Optical Character Recognition on Unbalanced Datasets |
| title_short | Focal CTC Loss for Chinese Optical Character Recognition on Unbalanced Datasets |
| title_sort | focal ctc loss for chinese optical character recognition on unbalanced datasets |
| url | http://dx.doi.org/10.1155/2019/9345861 |
| work_keys_str_mv | AT xinjiefeng focalctclossforchineseopticalcharacterrecognitiononunbalanceddatasets AT hongxunyao focalctclossforchineseopticalcharacterrecognitiononunbalanceddatasets AT shengpingzhang focalctclossforchineseopticalcharacterrecognitiononunbalanceddatasets |