Name
Abstract | ||
|---|---|---|
Scene text recognition has attracted particular research interest because it is a very challenging problem and has various applications. The most cutting-edge methods are attentional encoder-decoder frameworks that learn the alignment between the input image and output sequences. In particular, the decoder recurrently outputs predictions, using the prediction of the previous step as a guidance for every time step. In this study, we point out that the inappropriate use of previous predictions in existing attentional decoders restricts the recognition performance and brings instability. To handle this problem, we propose a novel module, namely adaptive embedding gate (AEG). The proposed AEG focuses on introducing high-order character language models to attentional decoders by controlling the information transmission between adjacent characters. AEG is a flexible module and can be easily integrated into the state-of-the-art attentional decoders for scene text recognition. We evaluate its effectiveness as well as robustness on a number of standard benchmarks, including the IIIT5K, SVT, SVT-P, CUTE80, and ICDAR datasets. Experimental results demonstrate that AEG can significantly boost recognition performance and bring better robustness. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2019.11.049 | Neurocomputing |
Keywords | Field | DocType |
Deep learning,Scene text recognition,Attention mechanism | Embedding,Robustness (computer science),Information transmission,Artificial intelligence,Machine learning,Text recognition,Mathematics,Language model | Journal |
Volume | ISSN | Citations |
381 | 0925-2312 | 4 |
PageRank | References | Authors |
0.39 | 48 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoxue Chen | 1 | 10 | 2.88 |
| Tianwei Wang | 2 | 10 | 4.97 |
| Yuanzhi Zhu | 3 | 16 | 2.91 |
| Lianwen Jin | 4 | 1337 | 113.14 |
| Canjie Luo | 5 | 54 | 9.32 |