Skip to content

Latest commit

 

History

History
66 lines (48 loc) · 4.77 KB

algorithm_overview_en.md

File metadata and controls

66 lines (48 loc) · 4.77 KB

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