GTC: Guided Training of CTC Towards Efficient and Accurate Scene Text Recognition Article Swipe
Wenyang Hu
,
Xiaocong Cai
,
Jun Hou
,
Shuai Yi
,
Zhiping Lin
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2002.01276
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2002.01276
Connectionist Temporal Classification (CTC) and attention mechanism are two main approaches used in recent scene text recognition works. Compared with attention-based methods, CTC decoder has a much shorter inference time, yet a lower accuracy. To design an efficient and effective model, we propose the guided training of CTC (GTC), where CTC model learns a better alignment and feature representations from a more powerful attentional guidance. With the benefit of guided training, CTC model achieves robust and accurate prediction for both regular and irregular scene text while maintaining a fast inference speed. Moreover, to further leverage the potential of CTC decoder, a graph convolutional network (GCN) is proposed to learn the local correlations of extracted features. Extensive experiments on standard benchmarks demonstrate that our end-to-end model achieves a new state-of-the-art for regular and irregular scene text recognition and needs 6 times shorter inference time than attentionbased methods.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2002.01276
- https://arxiv.org/pdf/2002.01276
- OA Status
- green
- Cited By
- 3
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3005397030
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https://openalex.org/W3005397030Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2002.01276Digital Object Identifier
- Title
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GTC: Guided Training of CTC Towards Efficient and Accurate Scene Text RecognitionWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-02-04Full publication date if available
- Authors
-
Wenyang Hu, Xiaocong Cai, Jun Hou, Shuai Yi, Zhiping LinList of authors in order
- Landing page
-
https://arxiv.org/abs/2002.01276Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2002.01276Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2002.01276Direct OA link when available
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Computer science, Inference, Leverage (statistics), Connectionism, Artificial intelligence, Graph, Pattern recognition (psychology), Convolutional neural network, Speech recognition, Machine learning, Artificial neural network, Theoretical computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2021: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.text | 15, 84, 134 |
| abstract_inverted_index.than | 143 |
| abstract_inverted_index.that | 121 |
| abstract_inverted_index.time | 142 |
| abstract_inverted_index.used | 11 |
| abstract_inverted_index.with | 19 |
| abstract_inverted_index.(CTC) | 3 |
| abstract_inverted_index.(GCN) | 104 |
| abstract_inverted_index.graph | 101 |
| abstract_inverted_index.learn | 108 |
| abstract_inverted_index.local | 110 |
| abstract_inverted_index.lower | 32 |
| abstract_inverted_index.model | 51, 72, 124 |
| abstract_inverted_index.needs | 137 |
| abstract_inverted_index.scene | 14, 83, 133 |
| abstract_inverted_index.time, | 29 |
| abstract_inverted_index.times | 139 |
| abstract_inverted_index.where | 49 |
| abstract_inverted_index.while | 85 |
| abstract_inverted_index.(GTC), | 48 |
| abstract_inverted_index.better | 54 |
| abstract_inverted_index.design | 35 |
| abstract_inverted_index.guided | 44, 69 |
| abstract_inverted_index.learns | 52 |
| abstract_inverted_index.model, | 40 |
| abstract_inverted_index.recent | 13 |
| abstract_inverted_index.robust | 74 |
| abstract_inverted_index.speed. | 90 |
| abstract_inverted_index.works. | 17 |
| abstract_inverted_index.benefit | 67 |
| abstract_inverted_index.decoder | 23 |
| abstract_inverted_index.feature | 57 |
| abstract_inverted_index.further | 93 |
| abstract_inverted_index.network | 103 |
| abstract_inverted_index.propose | 42 |
| abstract_inverted_index.regular | 80, 130 |
| abstract_inverted_index.shorter | 27, 140 |
| abstract_inverted_index.Compared | 18 |
| abstract_inverted_index.Temporal | 1 |
| abstract_inverted_index.accurate | 76 |
| abstract_inverted_index.achieves | 73, 125 |
| abstract_inverted_index.decoder, | 99 |
| abstract_inverted_index.leverage | 94 |
| abstract_inverted_index.methods, | 21 |
| abstract_inverted_index.methods. | 145 |
| abstract_inverted_index.powerful | 62 |
| abstract_inverted_index.proposed | 106 |
| abstract_inverted_index.standard | 118 |
| abstract_inverted_index.training | 45 |
| abstract_inverted_index.Extensive | 115 |
| abstract_inverted_index.Moreover, | 91 |
| abstract_inverted_index.accuracy. | 33 |
| abstract_inverted_index.alignment | 55 |
| abstract_inverted_index.attention | 5 |
| abstract_inverted_index.effective | 39 |
| abstract_inverted_index.efficient | 37 |
| abstract_inverted_index.extracted | 113 |
| abstract_inverted_index.features. | 114 |
| abstract_inverted_index.guidance. | 64 |
| abstract_inverted_index.inference | 28, 89, 141 |
| abstract_inverted_index.irregular | 82, 132 |
| abstract_inverted_index.mechanism | 6 |
| abstract_inverted_index.potential | 96 |
| abstract_inverted_index.training, | 70 |
| abstract_inverted_index.approaches | 10 |
| abstract_inverted_index.benchmarks | 119 |
| abstract_inverted_index.end-to-end | 123 |
| abstract_inverted_index.prediction | 77 |
| abstract_inverted_index.attentional | 63 |
| abstract_inverted_index.demonstrate | 120 |
| abstract_inverted_index.experiments | 116 |
| abstract_inverted_index.maintaining | 86 |
| abstract_inverted_index.recognition | 16, 135 |
| abstract_inverted_index.correlations | 111 |
| abstract_inverted_index.Connectionist | 0 |
| abstract_inverted_index.convolutional | 102 |
| abstract_inverted_index.Classification | 2 |
| abstract_inverted_index.attentionbased | 144 |
| abstract_inverted_index.attention-based | 20 |
| abstract_inverted_index.representations | 58 |
| abstract_inverted_index.state-of-the-art | 128 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |