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Two-stage Algorithm

1. 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 v2.0 models list.

1.1 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 85.80% 86.71% 86.25% Download link
EAST MobileNetV3 79.42% 80.64% 80.03% Download link
DB ResNet50_vd 86.41% 78.72% 82.38% Download link
DB MobileNetV3 77.29% 73.08% 75.12% Download link
SAST ResNet50_vd 91.39% 83.77% 87.42% Download link

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% 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:

For the training guide and use of PaddleOCR text detection algorithms, please refer to the document Text detection model training/evaluation/prediction

1.2 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 80.9% rec_r34_vd_none_none_ctc Download link
Rosetta MobileNetV3 78.05% rec_mv3_none_none_ctc Download link
CRNN Resnet34_vd 82.76% rec_r34_vd_none_bilstm_ctc Download link
CRNN MobileNetV3 79.97% rec_mv3_none_bilstm_ctc Download link
StarNet Resnet34_vd 84.44% rec_r34_vd_tps_bilstm_ctc Download link
StarNet MobileNetV3 81.42% rec_mv3_tps_bilstm_ctc Download link
RARE MobileNetV3 82.5% rec_mv3_tps_bilstm_att Download link
RARE Resnet34_vd 83.6% rec_r34_vd_tps_bilstm_att Download link
SRN Resnet50_vd_fpn 88.52% rec_r50fpn_vd_none_srn Download link
NRTR NRTR_MTB 84.3% rec_mtb_nrtr Download link

Please refer to the document for training guide and use of PaddleOCR

2. Training

For the training guide and use of PaddleOCR text detection algorithms, please refer to the document Text detection model training/evaluation/prediction. For text recognition algorithms, please refer to Text recognition model training/evaluation/prediction

3. Inference

Except for the PP-OCR series models of the above models, the other models only support inference based on the Python engine. For details, please refer to Inference based on Python prediction engine