Scene text recognition & vehicle license plate recognition
Scene text recognition and vehicle license plate recognition both fall into the same class of computer vision problem: text recognition. Text recognition tackles the problem where characters are recognized in sequence and the length of the characters is varying. Although many recent works are propos...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/140249 |
| _version_ | 1826110422816653312 |
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| author | Hu, Wen Yang |
| author2 | Lin Zhiping |
| author_facet | Lin Zhiping Hu, Wen Yang |
| author_sort | Hu, Wen Yang |
| collection | NTU |
| description | Scene text recognition and vehicle license plate recognition both fall into the same class of computer vision problem: text recognition. Text recognition tackles the problem where characters are recognized in sequence and the length of the characters is varying. Although many recent works are proposed to improve the performance of text recognizer, there still remains a research gap on the tradeoff between the recognition accuracy and the inference speed. This project focus on the novel algorithm design on scene text recognition and the vehicle license plate recognition practice on real-world applications. Guided Training of Connectionist Temporal Classification (GTC) is proposed to achieve effective and efficient recognition. Graph Convolutional Network (GCN) is also introduced to further improve the performance. Experimental results show that my approach achieves a new state-of-the-art accuracy on most scene text recognition benchmarks and it shows great robustness on vehicle license plate recognition on real world images. A software pipeline is also made to recognize arbitrary text. Our method was even accepted as a poster in Conference AAAI 2020. |
| first_indexed | 2024-10-01T02:34:10Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/140249 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:34:10Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1402492023-07-07T18:40:34Z Scene text recognition & vehicle license plate recognition Hu, Wen Yang Lin Zhiping School of Electrical and Electronic Engineering SenseTime Group Ltd. ezplin@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems Scene text recognition and vehicle license plate recognition both fall into the same class of computer vision problem: text recognition. Text recognition tackles the problem where characters are recognized in sequence and the length of the characters is varying. Although many recent works are proposed to improve the performance of text recognizer, there still remains a research gap on the tradeoff between the recognition accuracy and the inference speed. This project focus on the novel algorithm design on scene text recognition and the vehicle license plate recognition practice on real-world applications. Guided Training of Connectionist Temporal Classification (GTC) is proposed to achieve effective and efficient recognition. Graph Convolutional Network (GCN) is also introduced to further improve the performance. Experimental results show that my approach achieves a new state-of-the-art accuracy on most scene text recognition benchmarks and it shows great robustness on vehicle license plate recognition on real world images. A software pipeline is also made to recognize arbitrary text. Our method was even accepted as a poster in Conference AAAI 2020. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-27T08:47:55Z 2020-05-27T08:47:55Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/140249 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering::Computer hardware, software and systems Hu, Wen Yang Scene text recognition & vehicle license plate recognition |
| title | Scene text recognition & vehicle license plate recognition |
| title_full | Scene text recognition & vehicle license plate recognition |
| title_fullStr | Scene text recognition & vehicle license plate recognition |
| title_full_unstemmed | Scene text recognition & vehicle license plate recognition |
| title_short | Scene text recognition & vehicle license plate recognition |
| title_sort | scene text recognition vehicle license plate recognition |
| topic | Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | https://hdl.handle.net/10356/140249 |
| work_keys_str_mv | AT huwenyang scenetextrecognitionvehiclelicenseplaterecognition |