An Improved Deep Learning Network Structure for Multitask Text Implication Translation Character Recognition
With the rapid development of artificial intelligence technology, multitasking textual translation has attracted more and more attention. Especially after the application of deep learning technology, the performance of multitask translation text detection and recognition has been greatly improved. H...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6617799 |
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| _version_ | 1832547659064279040 |
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| author | Xiaoli Ma Hongyan Xu Xiaoqian Zhang Haoyong Wang |
| author_facet | Xiaoli Ma Hongyan Xu Xiaoqian Zhang Haoyong Wang |
| author_sort | Xiaoli Ma |
| collection | DOAJ |
| description | With the rapid development of artificial intelligence technology, multitasking textual translation has attracted more and more attention. Especially after the application of deep learning technology, the performance of multitask translation text detection and recognition has been greatly improved. However, because multitasking contains the interference problem faced by the translated text, there is a big gap between recognition performance and actual application requirements. Aiming at multitasking and translation text detection, this paper proposes a text localization method based on multichannel multiscale detection of the largest stable extreme value region and cascade filtering. This paper selects the appropriate color channel and scale to extract the maximum stable extreme value area as the character candidate area and designs a cascaded filter from coarse to fine to remove false detections. The coarse filter is based on some simple morphological features and stroke width features, and the fine filter is trained by a two-recognition convolutional neural network. The remaining character candidate regions are merged into horizontal or multidirectional character strings through the graph model. The experimental results on the text data set prove the effectiveness of the improved deep learning network character model and the feasibility of the textual implication translation analysis method based on this model. Among them, the text contains translation character recognition results prove that the model has good description ability. The characteristics of the model determine that this method is not sensitive to the scale of the sliding window, so it performs better than the existing typical methods in retrieval tasks. |
| format | Article |
| id | doaj-art-a4f18df5daac42ee8ca5cfb08df2dd4f |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a4f18df5daac42ee8ca5cfb08df2dd4f2025-02-03T06:43:57ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66177996617799An Improved Deep Learning Network Structure for Multitask Text Implication Translation Character RecognitionXiaoli Ma0Hongyan Xu1Xiaoqian Zhang2Haoyong Wang3Department of Foreign Language, Hebei Agricultural University, Baoding, Hebei 071001, ChinaDepartment of Foreign Language, Hebei Agricultural University, Baoding, Hebei 071001, ChinaDepartment of Foreign Language, Hebei Agricultural University, Baoding, Hebei 071001, ChinaDepartment of Foreign Language, Hebei Agricultural University, Baoding, Hebei 071001, ChinaWith the rapid development of artificial intelligence technology, multitasking textual translation has attracted more and more attention. Especially after the application of deep learning technology, the performance of multitask translation text detection and recognition has been greatly improved. However, because multitasking contains the interference problem faced by the translated text, there is a big gap between recognition performance and actual application requirements. Aiming at multitasking and translation text detection, this paper proposes a text localization method based on multichannel multiscale detection of the largest stable extreme value region and cascade filtering. This paper selects the appropriate color channel and scale to extract the maximum stable extreme value area as the character candidate area and designs a cascaded filter from coarse to fine to remove false detections. The coarse filter is based on some simple morphological features and stroke width features, and the fine filter is trained by a two-recognition convolutional neural network. The remaining character candidate regions are merged into horizontal or multidirectional character strings through the graph model. The experimental results on the text data set prove the effectiveness of the improved deep learning network character model and the feasibility of the textual implication translation analysis method based on this model. Among them, the text contains translation character recognition results prove that the model has good description ability. The characteristics of the model determine that this method is not sensitive to the scale of the sliding window, so it performs better than the existing typical methods in retrieval tasks.http://dx.doi.org/10.1155/2021/6617799 |
| spellingShingle | Xiaoli Ma Hongyan Xu Xiaoqian Zhang Haoyong Wang An Improved Deep Learning Network Structure for Multitask Text Implication Translation Character Recognition Complexity |
| title | An Improved Deep Learning Network Structure for Multitask Text Implication Translation Character Recognition |
| title_full | An Improved Deep Learning Network Structure for Multitask Text Implication Translation Character Recognition |
| title_fullStr | An Improved Deep Learning Network Structure for Multitask Text Implication Translation Character Recognition |
| title_full_unstemmed | An Improved Deep Learning Network Structure for Multitask Text Implication Translation Character Recognition |
| title_short | An Improved Deep Learning Network Structure for Multitask Text Implication Translation Character Recognition |
| title_sort | improved deep learning network structure for multitask text implication translation character recognition |
| url | http://dx.doi.org/10.1155/2021/6617799 |
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