Scene Text Recognition on IIIT5K 3000 (test)
99.1AccuracyPARSeq_A
Evaluation Results
| Method | Links | |
|---|---|---|
| PARSeq_ATrain data=R2022.07 | 99.1 | |
| TRBATrain data=R2022.07 | 98.6 | |
| ABINetTrain data=R2022.07 | 98.6 | |
| PARSeq_NTrain data=R2022.07 | 98.3 | |
| VITSTR-STrain data=R2022.07 | 98.1 | |
| PARSeq_ATrain data=S2022.07 | 97 | |
| PARSeqType=VL, Backbone=DeiT, Structure=Transformer, Size=32×1282022.03 | 97 | |
| LevOCRType=VL, Backbone=ResNet45, Structure=ResNet, Size=32×1282022.03 | 96.6 | |
| SIGATType=V, Backbone=ViT-B, Structure=Transformer, Size=32×1282022.03 | 96.6 | |
| ABINet+ConCLRType=VL, Backbone=ResNet45-Trns, Structure=Transformer, Size=32×1282022.03 | 96.5 | |
| TRBATrain data=S2022.07 | 96.3 | |
| ABINetTrain data=S32022.07 | 96.2 | |
| ABINetType=VL, Backbone=ResNet45-Trns, Structure=Transformer, Size=32×1282022.03 | 96.2 | |
| SIGARType=V, Backbone=ResNet45, Structure=ResNet, Size=32×1282022.03 | 95.9 | |
| VisionLANTrain data=S2022.07 | 95.8 | |
| VisionLANType=VL, Backbone=ResNet45, Structure=ResNet, Size=64×2562022.03 | 95.8 | |
| S-GTRType=VL, Backbone=ResNet50Dilated-PPM, Structure=ResNet, Size=64×2562022.03 | 95.8 | |
| TextScannerTrain data=S*2022.07 | 95.7 | |
| PARSeq_NTrain data=S2022.07 | 95.7 | |
| PREN2DTrain data=S2022.07 | 95.6 | |
| LevOCRType=VL, Backbone=ViT, Structure=Transformer, Size=32×1282022.03 | 95.6 | |
| Robust ScannerTrain data=S,B2022.07 | 95.4 | |
| ABINetTrain data=S2022.07 | 95.3 | |
| Bhunia et al.Train data=S2022.07 | 95.2 | |
| CVAE-FeedTrain data=S2022.07 | 95.2 | |
| Bhunia et al.Type=VL, Backbone=ResNet50-FPN, Structure=ResNet, Size=32×1002022.03 | 95.2 | |
| RCEEDTrain data=S,B2022.07 | 94.9 | |
| SRNTrain data=S2022.07 | 94.8 | |
| SRNType=VL, Backbone=ResNet50-FPN, Structure=ResNet, Size=64×2562022.03 | 94.8 | |
| AutoSTRTrain data=S2022.07 | 94.7 | |
| CRNNTrain data=R2022.07 | 94.6 | |
| PlugNetTrain data=S2022.07 | 94.4 | |
| STN-CSTRTrain data=S2022.07 | 94.2 | |
| VITSTR-STrain data=S2022.07 | 94 | |
| TRBATrain data=S2022.07 | 92.1 | |
| CRNNTrain data=S2022.07 | 91.2 | |
| VITSTR-BTrain data=S22022.07 | 88.4 | |
| CRNNTrain data=S2022.07 | 84.3 | |
| SRNTraining Data=MJ+ST, Annotations=word2021.02 | 0.948 | |
| DANTraining Data=MJ+ST, Annotations=word2021.02 | 0.943 | |
| STN-CSTRTraining Data=MJ+ST, Annotations=word, STN=true2021.02 | 0.942 | |
| TextScannerTraining Data=MJ+ST, Annotations=word,char2021.02 | 0.939 | |
| SEEDTraining Data=MJ+ST, Annotations=word, Beam search=true2021.02 | 0.938 | |
| CSTRTraining Data=MJ+ST, Annotations=word, STN=false2021.02 | 0.937 | |
| ASTERTraining Data=MJ+ST, Annotations=word, Beam search=true2021.02 | 0.934 | |
| SATRNTraining Data=MJ+ST, Annotations=word2021.02 | 0.928 | |
| CA-FCNTraining Data=ST, Annotations=word,char2021.02 | 0.919 | |
| SARTraining Data=MJ+ST, Annotations=word, Test-Time Augmentation (TTA)=true2021.02 | 0.915 | |
| Baek et al.Training Data=MJ+ST, Annotations=word2021.02 | 0.879 | |
| FANTraining Data=MJ+ST, Annotations=word2021.02 | 0.874 | |
| AONTraining Data=MJ+ST, Annotations=word2021.02 | 0.87 |