Overview

Weights

Here are the list of weights available for Inference.

For the ease of reference, some weights may have shorter aliases, which will be separated by / in the table. For example, “DB_r18 / dbnet_resnet18_fpnc_1200e_icdar2015” means that you can use either DB_r18 or dbnet_resnet18_fpnc_1200e_icdar2015 to initialize the Inferencer:

>>> from mmocr.apis import TextDetInferencer
>>> inferencer = TextDetInferencer(model='DB_r18')
>>> # equivalent to
>>> inferencer = TextDetInferencer(model='dbnet_resnet18_fpnc_1200e_icdar2015')

Text Detection

Model

README

ICDAR2015 (hmean-iou)

CTW1500 (hmean-iou)

Totaltext (hmean-iou)

DB_r18 / dbnet_resnet18_fpnc_1200e_icdar2015

link

0.8169

-

-

dbnet_resnet50_fpnc_1200e_icdar2015

link

0.8504

-

-

dbnet_resnet50-dcnv2_fpnc_1200e_icdar2015

link

0.8543

-

-

DB_r50 / DBNet / dbnet_resnet50-oclip_fpnc_1200e_icdar2015

link

0.8644

-

-

dbnet_resnet18_fpnc_1200e_totaltext

link

-

-

0.8182

DBPP_r50 / dbnetpp_resnet50_fpnc_1200e_icdar2015

link

0.8622

-

-

dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015

link

0.8684

-

-

DBNetpp / dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015

link

0.8882

-

-

MaskRCNN_CTW / mask-rcnn_resnet50_fpn_160e_ctw1500

link

-

0.7458

-

mask-rcnn_resnet50-oclip_fpn_160e_ctw1500

link

-

0.7562

-

MaskRCNN_IC15 / mask-rcnn_resnet50_fpn_160e_icdar2015

link

0.8182

-

-

MaskRCNN / mask-rcnn_resnet50-oclip_fpn_160e_icdar2015

link

0.8513

-

-

DRRG / drrg_resnet50_fpn-unet_1200e_ctw1500

link

-

0.8467

-

FCE_CTW_DCNv2 / fcenet_resnet50-dcnv2_fpn_1500e_ctw1500

link

-

0.8488

-

fcenet_resnet50-oclip_fpn_1500e_ctw1500

link

-

0.8192

-

FCE_IC15 / fcenet_resnet50_fpn_1500e_icdar2015

link

0.8528

-

-

FCENet / fcenet_resnet50-oclip_fpn_1500e_icdar2015

link

0.8604

-

-

fcenet_resnet50_fpn_1500e_totaltext

link

-

-

0.8134

PANet_CTW / panet_resnet18_fpem-ffm_600e_ctw1500

link

-

0.777

-

PANet_IC15 / panet_resnet18_fpem-ffm_600e_icdar2015

link

0.7848

-

-

PS_CTW / psenet_resnet50_fpnf_600e_ctw1500

link

-

0.7793

-

psenet_resnet50-oclip_fpnf_600e_ctw1500

link

-

0.8037

-

PS_IC15 / psenet_resnet50_fpnf_600e_icdar2015

link

0.7998

-

-

PSENet / psenet_resnet50-oclip_fpnf_600e_icdar2015

link

0.8478

-

-

textsnake_resnet50_fpn-unet_1200e_ctw1500

link

-

0.8286

-

TextSnake / textsnake_resnet50-oclip_fpn-unet_1200e_ctw1500

link

-

0.8529

-

Text Recognition

Note

Avg is the average on IIIT5K, SVT, ICDAR2013, ICDAR2015, SVTP, CT80.

Model

README

Avg (word_acc)

IIIT5K (word_acc)

SVT (word_acc)

ICDAR2013 (word_acc)

ICDAR2015 (word_acc)

SVTP (word_acc)

CT80 (word_acc)

ABINet_Vision / abinet-vision_20e_st-an_mj

link

0.88

0.95

0.91

0.94

0.79

0.84

0.84

ABINet / abinet_20e_st-an_mj

link

0.91

0.96

0.94

0.95

0.81

0.89

0.88

ASTER / aster_resnet45_6e_st_mj

link

0.86

0.94

0.89

0.93

0.77

0.81

0.85

CRNN / crnn_mini-vgg_5e_mj

link

0.70

0.81

0.81

0.87

0.56

0.61

0.57

MASTER / master_resnet31_12e_st_mj_sa

link

0.88

0.95

0.90

0.95

0.76

0.85

0.89

nrtr_modality-transform_6e_st_mj

link

0.83

0.92

0.88

0.94

0.72

0.78

0.75

NRTR / NRTR_1/8-1/4 / nrtr_resnet31-1by8-1by4_6e_st_mj

link

0.87

0.95

0.88

0.95

0.76

0.80

0.89

NRTR_1/16-1/8 / nrtr_resnet31-1by16-1by8_6e_st_mj

link

0.87

0.95

0.90

0.94

0.74

0.80

0.89

svtr-small / svtr-small_20e_st_mj

link

0.86

0.86

0.90

0.94

0.75

0.85

0.89

svtr-base / svtr-base_20e_st_mj

link

0.87

0.86

0.92

0.94

0.74

0.84

0.90

RobustScanner / robustscanner_resnet31_5e_st-sub_mj-sub_sa_real

link

0.87

0.95

0.89

0.93

0.76

0.81

0.87

SAR / sar_resnet31_parallel-decoder_5e_st-sub_mj-sub_sa_real

link

0.88

0.95

0.88

0.94

0.76

0.83

0.90

sar_resnet31_sequential-decoder_5e_st-sub_mj-sub_sa_real

link

0.87

0.96

0.87

0.94

0.77

0.81

0.89

SATRN / satrn_shallow_5e_st_mj

link

0.90

0.96

0.92

0.96

0.80

0.88

0.90

SATRN_sm / satrn_shallow-small_5e_st_mj

link

0.88

0.94

0.90

0.96

0.79

0.86

0.85

Key Information Extraction

Model

README

wildreceipt (macro_f1)

SDMGR / sdmgr_unet16_60e_wildreceipt

link

0.89

sdmgr_novisual_60e_wildreceipt

link

0.87

sdmgr_novisual_60e_wildreceipt_openset

link

0.93

Statistics

• Number of checkpoints: 48

• Number of configs: 49

• Number of papers: 19

  • ALGORITHM: 19

BackBones

• Number of checkpoints: 1

• Number of configs: 0

• Number of papers: 1

  • [ALGORITHM] Language Matters: A Weakly Supervised Vision-Language Pre-Training Approach for Scene Text Detection and Spotting

Text Detection Models

• Number of checkpoints: 29

• Number of configs: 29

• Number of papers: 8

  • [ALGORITHM] Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection

  • [ALGORITHM] Efficient and Accurate Arbitrary-Shaped Text Detection With Pixel Aggregation Network

  • [ALGORITHM] Fourier Contour Embedding for Arbitrary-Shaped Text Detection

  • [ALGORITHM] Mask R-CNN

  • [ALGORITHM] Real-Time Scene Text Detection With Differentiable Binarization and Adaptive Scale Fusion

  • [ALGORITHM] Real-Time Scene Text Detection With Differentiable Binarization

  • [ALGORITHM] Shape Robust Text Detection With Progressive Scale Expansion Network

  • [ALGORITHM] Textsnake: A Flexible Representation for Detecting Text of Arbitrary Shapes

Text Recognition Models

• Number of checkpoints: 16

• Number of configs: 17

• Number of papers: 9

  • [ALGORITHM] An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition

  • [ALGORITHM] Aster: An Attentional Scene Text Recognizer With Flexible Rectification

  • [ALGORITHM] Master: Multi-Aspect Non-Local Network for Scene Text Recognition

  • [ALGORITHM] Nrtr: A No-Recurrence Sequence-to-Sequence Model for Scene Text Recognition

  • [ALGORITHM] On Recognizing Texts of Arbitrary Shapes With 2d Self-Attention

  • [ALGORITHM] Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition

  • [ALGORITHM] Robustscanner: Dynamically Enhancing Positional Clues for Robust Text Recognition

  • [ALGORITHM] Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition

  • [ALGORITHM] Svtr: Scene Text Recognition With a Single Visual Model

Key Information Extraction Models

• Number of checkpoints: 3

• Number of configs: 3

• Number of papers: 1

  • [ALGORITHM] Spatial Dual-Modality Graph Reasoning for Key Information Extraction