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Algorithm introduction
This tutorial lists the text detection algorithms and text recognition 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 v1.1 models list.
1. Text Detection Algorithm
PaddleOCR open source text detection algorithms list:
On the ICDAR2015 dataset, the text detection result is as follows:
| Model | Backbone | precision | recall | Hmean | Download link |
|---|---|---|---|---|---|
| EAST | ResNet50_vd | 88.18% | 85.51% | 86.82% | Download link |
| EAST | MobileNetV3 | 81.67% | 79.83% | 80.74% | Download link |
| DB | ResNet50_vd | 83.79% | 80.65% | 82.19% | Download link |
| DB | MobileNetV3 | 75.92% | 73.18% | 74.53% | Download link |
| SAST | ResNet50_vd | 92.18% | 82.96% | 87.33% | Download link |
On Total-Text dataset, the text detection result is as follows:
| Model | Backbone | precision | recall | Hmean | Download link |
|---|---|---|---|---|---|
| SAST | ResNet50_vd | 88.74% | 79.80% | 84.03% | Download link |
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).
For the training guide and use of PaddleOCR text detection algorithms, please refer to the document Text detection model training/evaluation/prediction
2. Text Recognition Algorithm
PaddleOCR open-source text recognition algorithms list:
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 | 80.24% | rec_r34_vd_none_none_ctc | Download link |
| Rosetta | MobileNetV3 | 78.16% | rec_mv3_none_none_ctc | Download link |
| CRNN | Resnet34_vd | 82.20% | rec_r34_vd_none_bilstm_ctc | Download link |
| CRNN | MobileNetV3 | 79.37% | rec_mv3_none_bilstm_ctc | Download link |
| STAR-Net | Resnet34_vd | 83.93% | rec_r34_vd_tps_bilstm_ctc | Download link |
| STAR-Net | MobileNetV3 | 81.56% | rec_mv3_tps_bilstm_ctc | Download link |
| RARE | Resnet34_vd | 84.90% | rec_r34_vd_tps_bilstm_attn | Download link |
| RARE | MobileNetV3 | 83.32% | rec_mv3_tps_bilstm_attn | Download link |
| SRN | Resnet50_vd_fpn | 88.33% | rec_r50fpn_vd_none_srn | Download link |
Note： SRN model uses data expansion method to expand the two training sets mentioned above, and the expanded data can be downloaded from Baidu Drive (download code: y3ry).
The average accuracy of the two-stage training in the original paper is 89.74%, and that of one stage training in paddleocr is 88.33%. Both pre-trained weights can be downloaded here.
Please refer to the document for training guide and use of PaddleOCR text recognition algorithms Text recognition model training/evaluation/prediction