Scene Text Recognition Based on Improved CRNN
Text recognition is an important research topic in computer vision. Scene text, which refers to the text in real scenes, sometimes needs to meet the requirement of attracting attention, and there is the situation such as deformation. At the same time, the image acquisition process is affected by fac...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/14/7/369 |
| _version_ | 1797588919183736832 |
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| author | Wenhua Yu Mayire Ibrayim Askar Hamdulla |
| author_facet | Wenhua Yu Mayire Ibrayim Askar Hamdulla |
| author_sort | Wenhua Yu |
| collection | DOAJ |
| description | Text recognition is an important research topic in computer vision. Scene text, which refers to the text in real scenes, sometimes needs to meet the requirement of attracting attention, and there is the situation such as deformation. At the same time, the image acquisition process is affected by factors such as occlusion, noise, and obstruction, making scene text recognition tasks more challenging. In this paper, we improve the CRNN model for text recognition, which has relatively low accuracy, poor performance in recognizing irregular text, and only considers obtaining text sequence information from a single aspect, resulting in incomplete information acquisition. Firstly, to address the problems of low text recognition accuracy and poor recognition of irregular text, we add label smoothing to ensure the model’s generalization ability. Then, we introduce the smoothing loss function from speech recognition into the field of text recognition, and add a language model to increase information acquisition channels, ultimately achieving the goal of improving text recognition accuracy. This method was experimentally verified on six public datasets and compared with other advanced methods. The experimental results show that this method performs well in most benchmark tests, and the improved model outperforms the original model in recognition performance. |
| first_indexed | 2024-03-11T00:59:54Z |
| format | Article |
| id | doaj.art-981408c576544261b9dbb68ed636cf98 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-11T00:59:54Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-981408c576544261b9dbb68ed636cf982023-11-18T19:46:37ZengMDPI AGInformation2078-24892023-06-0114736910.3390/info14070369Scene Text Recognition Based on Improved CRNNWenhua Yu0Mayire Ibrayim1Askar Hamdulla2College of Information Science and Engineering, Xinjiang University, Urumqi 830017, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830017, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830017, ChinaText recognition is an important research topic in computer vision. Scene text, which refers to the text in real scenes, sometimes needs to meet the requirement of attracting attention, and there is the situation such as deformation. At the same time, the image acquisition process is affected by factors such as occlusion, noise, and obstruction, making scene text recognition tasks more challenging. In this paper, we improve the CRNN model for text recognition, which has relatively low accuracy, poor performance in recognizing irregular text, and only considers obtaining text sequence information from a single aspect, resulting in incomplete information acquisition. Firstly, to address the problems of low text recognition accuracy and poor recognition of irregular text, we add label smoothing to ensure the model’s generalization ability. Then, we introduce the smoothing loss function from speech recognition into the field of text recognition, and add a language model to increase information acquisition channels, ultimately achieving the goal of improving text recognition accuracy. This method was experimentally verified on six public datasets and compared with other advanced methods. The experimental results show that this method performs well in most benchmark tests, and the improved model outperforms the original model in recognition performance.https://www.mdpi.com/2078-2489/14/7/369CRNNtext recognitionlabel smoothinglanguage modeldeep learning |
| spellingShingle | Wenhua Yu Mayire Ibrayim Askar Hamdulla Scene Text Recognition Based on Improved CRNN Information CRNN text recognition label smoothing language model deep learning |
| title | Scene Text Recognition Based on Improved CRNN |
| title_full | Scene Text Recognition Based on Improved CRNN |
| title_fullStr | Scene Text Recognition Based on Improved CRNN |
| title_full_unstemmed | Scene Text Recognition Based on Improved CRNN |
| title_short | Scene Text Recognition Based on Improved CRNN |
| title_sort | scene text recognition based on improved crnn |
| topic | CRNN text recognition label smoothing language model deep learning |
| url | https://www.mdpi.com/2078-2489/14/7/369 |
| work_keys_str_mv | AT wenhuayu scenetextrecognitionbasedonimprovedcrnn AT mayireibrayim scenetextrecognitionbasedonimprovedcrnn AT askarhamdulla scenetextrecognitionbasedonimprovedcrnn |