This tutorial lists the OCR algorithms supported by PaddleOCR, as well as the models and metrics of each algorithm on English public datasets. It is mainly used for algorithm introduction and algorithm performance comparison. For more models on other datasets including Chinese, please refer to PP-OCR v2.0 models list.
Supported text detection algorithms (Click the link to get the tutorial):
On the ICDAR2015 dataset, the text detection result is as follows:
| Model | Backbone | Precision | Recall | Hmean | Download link |
|---|---|---|---|---|---|
| EAST | ResNet50_vd | 88.71% | 81.36% | 84.88% | trained model |
| EAST | MobileNetV3 | 78.2% | 79.1% | 78.65% | trained model |
| DB | ResNet50_vd | 86.41% | 78.72% | 82.38% | trained model |
| DB | MobileNetV3 | 77.29% | 73.08% | 75.12% | trained model |
| SAST | ResNet50_vd | 91.39% | 83.77% | 87.42% | trained model |
| PSE | ResNet50_vd | 85.81% | 79.53% | 82.55% | trianed model |
| PSE | MobileNetV3 | 82.20% | 70.48% | 75.89% | trianed model |
On Total-Text dataset, the text detection result is as follows:
| Model | Backbone | Precision | Recall | Hmean | Download link |
|---|---|---|---|---|---|
| SAST | ResNet50_vd | 89.63% | 78.44% | 83.66% | trained model |
On CTW1500 dataset, the text detection result is as follows:
| Model | Backbone | Precision | Recall | Hmean | Download link |
|---|---|---|---|---|---|
| FCE | ResNet50_dcn | 88.39% | 82.18% | 85.27% | trained model |
Note： Additional data, like icdar2013, icdar2017, COCO-Text, ArT, was added to the model training of SAST. Download English public dataset in organized format used by PaddleOCR from:
- Baidu Drive (download code: 2bpi).
- Google Drive
Supported text recognition algorithms (Click the link to get the tutorial):
Refer to DTRB, the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow:
| Model | Backbone | Avg Accuracy | Module combination | Download link |
|---|---|---|---|---|
| Rosetta | Resnet34_vd | 79.11% | rec_r34_vd_none_none_ctc | trained model |
| Rosetta | MobileNetV3 | 75.80% | rec_mv3_none_none_ctc | trained model |
| CRNN | Resnet34_vd | 81.04% | rec_r34_vd_none_bilstm_ctc | trained model |
| CRNN | MobileNetV3 | 77.95% | rec_mv3_none_bilstm_ctc | trained model |
| StarNet | Resnet34_vd | 82.85% | rec_r34_vd_tps_bilstm_ctc | trained model |
| StarNet | MobileNetV3 | 79.28% | rec_mv3_tps_bilstm_ctc | trained model |
| RARE | Resnet34_vd | 83.98% | rec_r34_vd_tps_bilstm_att | trained model |
| RARE | MobileNetV3 | 81.76% | rec_mv3_tps_bilstm_att | trained model |
| SRN | Resnet50_vd_fpn | 86.31% | rec_r50fpn_vd_none_srn | trained model |
| NRTR | NRTR_MTB | 84.21% | rec_mtb_nrtr | trained model |
| SAR | Resnet31 | 87.20% | rec_r31_sar | trained model |
| SEED | Aster_Resnet | 85.35% | rec_resnet_stn_bilstm_att | trained model |
| SVTR | SVTR-Tiny | 89.25% | rec_svtr_tiny_none_ctc_en | trained model |
Supported end-to-end algorithms (Click the link to get the tutorial):