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Research map / Vision-language and multimodal / Human and embodied modeling

Robust Scene Text Recognition With Automatic Rectification

CVPR 2016 · Vision-language and multimodal › Human and embodied modeling · approach: Neural image captioning · 710 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add TPS-based STN rectification to the SRN (ablation)RARE · IIIT5K (None)word accuracy: 79.7 -> 81.9Helped
Add TPS-based STN rectification to the SRN (ablation)RARE · IIIT5K (50)word accuracy: 96.5 -> 96.2Hurt
Add TPS-based STN rectification to the SRN (ablation)RARE · IIIT5K (1k)word accuracy: 92.8 -> 93.8Helped
Add TPS-based STN rectification to the SRN (ablation)RARE · SVT (50)word accuracy: 96.1 -> 95.5Hurt
Add TPS-based STN rectification to the SRN (ablation)RARE · SVT (None)word accuracy: 81.5 -> 81.9Helped
Add TPS-based STN rectification to the SRN (ablation)RARE · IC03 (50)word accuracy: 97.8 -> 98.3Helped
Add TPS-based STN rectification to the SRN (ablation)RARE · IC03 (Full)word accuracy: 96.4 -> 96.2Hurt
Add TPS-based STN rectification to the SRN (ablation)RARE · IC03 (50k)word accuracy: 93.7 -> 94.8Helped
Add TPS-based STN rectification to the SRN (ablation)RARE · IC03 (None)word accuracy: 88.7 -> 90.1Helped
Add TPS-based STN rectification to the SRN (ablation)RARE · IC13 (None)word accuracy: 87.5 -> 88.6Helped
Use RARE rather than Shi et al.scene text recognition · SVT-Perspective (Full)word accuracy: 72.6 -> 77.4Helped
Use RARE rather than Shi et al.scene text recognition · SVT-Perspective (None)word accuracy: 66.8 -> 71.8Helped

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