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[Enhancement] Update textrecog config and readme (#1597) 

* [Dataset Preparer] Add TextSpottingConfigGenerator

* update init

* [Enhancement] Update textrecog configs and raedme

* cfg

* fix
pull/1604/head
Tong Gao 2022-12-06 14:01:39 +08:00 committed by GitHub
parent 08cab32832
commit fa4fd1fd42
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14 changed files with 74 additions and 53 deletions
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4
configs/textrecog/_base_/datasets/cute80.py
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@ -1,8 +1,8 @@
cute80_textrecog_data_root = 'data/rec/ct80/'
cute80_textrecog_data_root = 'data/cute80'
cute80_textrecog_test = dict(
type='OCRDataset',
data_root=cute80_textrecog_data_root,
ann_file='test_labels.json',
ann_file='textrecog_test.json',
test_mode=True,
pipeline=None)

14
configs/textrecog/_base_/datasets/icdar2013.py
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@ -1,15 +1,21 @@
icdar2013_textrecog_data_root = 'data/rec/icdar_2013/'
icdar2013_textrecog_data_root = 'data/icdar2013'
icdar2013_textrecog_train = dict(
type='OCRDataset',
data_root=icdar2013_textrecog_data_root,
ann_file='train_labels.json',
test_mode=False,
ann_file='textrecog_train.json',
pipeline=None)
icdar2013_textrecog_test = dict(
type='OCRDataset',
data_root=icdar2013_textrecog_data_root,
ann_file='test_labels.json',
ann_file='textrecog_test.json',
test_mode=True,
pipeline=None)
icdar2013_857_textrecog_test = dict(
type='OCRDataset',
data_root=icdar2013_textrecog_data_root,
ann_file='textrecog_test_857.json',
test_mode=True,
pipeline=None)

14
configs/textrecog/_base_/datasets/icdar2015.py
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@ -1,15 +1,21 @@
icdar2015_textrecog_data_root = 'data/rec/icdar_2015/'
icdar2015_textrecog_data_root = 'data/icdar2015'
icdar2015_textrecog_train = dict(
type='OCRDataset',
data_root=icdar2015_textrecog_data_root,
ann_file='train_labels.json',
test_mode=False,
ann_file='textrecog_train.json',
pipeline=None)
icdar2015_textrecog_test = dict(
type='OCRDataset',
data_root=icdar2015_textrecog_data_root,
ann_file='test_labels.json',
ann_file='textrecog_test.json',
test_mode=True,
pipeline=None)
icdar2015_1811_textrecog_test = dict(
type='OCRDataset',
data_root=icdar2015_textrecog_data_root,
ann_file='textrecog_test_1811.json',
test_mode=True,
pipeline=None)

7
configs/textrecog/_base_/datasets/iiit5k.py
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@ -1,15 +1,14 @@
iiit5k_textrecog_data_root = 'data/rec/IIIT5K/'
iiit5k_textrecog_data_root = 'data/iiit5k'
iiit5k_textrecog_train = dict(
type='OCRDataset',
data_root=iiit5k_textrecog_data_root,
ann_file='train_labels.json',
test_mode=False,
ann_file='textrecog_train.json',
pipeline=None)
iiit5k_textrecog_test = dict(
type='OCRDataset',
data_root=iiit5k_textrecog_data_root,
ann_file='test_labels.json',
ann_file='textrecog_test.json',
test_mode=True,
pipeline=None)

10
configs/textrecog/_base_/datasets/svt.py
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@ -1,8 +1,14 @@
svt_textrecog_data_root = 'data/rec/svt/'
svt_textrecog_data_root = 'data/svt'
svt_textrecog_train = dict(
type='OCRDataset',
data_root=svt_textrecog_data_root,
ann_file='textrecog_train.json',
pipeline=None)
svt_textrecog_test = dict(
type='OCRDataset',
data_root=svt_textrecog_data_root,
ann_file='test_labels.json',
ann_file='textrecog_test.json',
test_mode=True,
pipeline=None)

10
configs/textrecog/_base_/datasets/svtp.py
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@ -1,8 +1,14 @@
svtp_textrecog_data_root = 'data/rec/svtp/'
svtp_textrecog_data_root = 'data/svtp'
svtp_textrecog_train = dict(
type='OCRDataset',
data_root=svtp_textrecog_data_root,
ann_file='textrecog_train.json',
pipeline=None)
svtp_textrecog_test = dict(
type='OCRDataset',
data_root=svtp_textrecog_data_root,
ann_file='test_labels.json',
ann_file='textrecog_test.json',
test_mode=True,
pipeline=None)

10
configs/textrecog/abinet/README.md
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@ -34,11 +34,11 @@ Linguistic knowledge is of great benefit to scene text recognition. However, how
## Results and models
| methods | pretrained | | Regular Text | | | Irregular Text | | download |
| :----------------------------------------------: | :--------------------------------------------------: | :----: | :----------: | :----: | :----: | :------------: | :----: | :------------------------------------------------- |
| | | IIIT5K | SVT | IC13 | IC15 | SVTP | CT80 | |
| [ABINet-Vision](/configs/textrecog/abinet/abinet-vision_20e_st-an_mj.py) | - | 0.9523 | 0.9057 | 0.9369 | 0.7886 | 0.8403 | 0.8437 | [model](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet-vision_20e_st-an_mj/abinet-vision_20e_st-an_mj_20220915_152445-85cfb03d.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet-vision_20e_st-an_mj/20220915_152445.log) |
| [ABINet](/configs/textrecog/abinet/abinet_20e_st-an_mj.py) | [Pretrained](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_pretrain-45deac15.pth) | 0.9603 | 0.9382 | 0.9547 | 0.8122 | 0.8868 | 0.8785 | [model](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_20e_st-an_mj/abinet_20e_st-an_mj_20221005_012617-ead8c139.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_20e_st-an_mj/20221005_012617.log) |
| methods | pretrained | | Regular Text | | | Irregular Text | | download |
| :--------------------------------------------: | :------------------------------------------------: | :----: | :----------: | :-------: | :-------: | :------------: | :----: | :----------------------------------------------- |
| | | IIIT5K | SVT | IC13-1015 | IC15-2077 | SVTP | CT80 | |
| [ABINet-Vision](/configs/textrecog/abinet/abinet-vision_20e_st-an_mj.py) | - | 0.9523 | 0.9196 | 0.9369 | 0.7896 | 0.8403 | 0.8437 | [model](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet-vision_20e_st-an_mj/abinet-vision_20e_st-an_mj_20220915_152445-85cfb03d.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet-vision_20e_st-an_mj/20220915_152445.log) |
| [ABINet](/configs/textrecog/abinet/abinet_20e_st-an_mj.py) | [Pretrained](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_pretrain-45deac15.pth) | 0.9603 | 0.9397 | 0.9557 | 0.8146 | 0.8868 | 0.8785 | [model](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_20e_st-an_mj/abinet_20e_st-an_mj_20221005_012617-ead8c139.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_20e_st-an_mj/20221005_012617.log) |
```{note}
1. ABINet allows its encoder to run and be trained without decoder and fuser. Its encoder is designed to recognize texts as a stand-alone model and therefore can work as an independent text recognizer. We release it as ABINet-Vision.

8
configs/textrecog/crnn/README.md
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@ -33,10 +33,10 @@ Image-based sequence recognition has been a long-standing research topic in comp
## Results and models
| methods | | Regular Text | | | | Irregular Text | | download |
| :----------------------------------------------------: | :----: | :----------: | :----: | :-: | :----: | :------------: | :----: | :-------------------------------------------------------------------------------------------: |
| methods | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
| [CRNN](/configs/textrecog/crnn/crnn_mini-vgg_5e_mj.py) | 0.8053 | 0.8053 | 0.8739 | | 0.5556 | 0.6093 | 0.5694 | [model](https://download.openmmlab.com/mmocr/textrecog/crnn/crnn_mini-vgg_5e_mj/crnn_mini-vgg_5e_mj_20220826_224120-8afbedbb.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/crnn/crnn_mini-vgg_5e_mj/20220826_224120.log) |
| methods | | Regular Text | | | | Irregular Text | | download |
| :----------------------------------------------------: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :-------------------------------------------------------------------------------------: |
| methods | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | |
| [CRNN](/configs/textrecog/crnn/crnn_mini-vgg_5e_mj.py) | 0.8053 | 0.7991 | 0.8739 | | 0.5571 | 0.6093 | 0.5694 | [model](https://download.openmmlab.com/mmocr/textrecog/crnn/crnn_mini-vgg_5e_mj/crnn_mini-vgg_5e_mj_20220826_224120-8afbedbb.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/crnn/crnn_mini-vgg_5e_mj/20220826_224120.log) |
## Citation

2
configs/textrecog/crnn/crnn_mini-vgg_5e_mj.py
View File

@ -11,7 +11,6 @@ _base_ = [
'../_base_/schedules/schedule_adadelta_5e.py',
'_base_crnn_mini-vgg.py',
]
# dataset settings
train_list = [_base_.mjsynth_textrecog_test]
test_list = [
@ -21,7 +20,6 @@ test_list = [
]
default_hooks = dict(logger=dict(type='LoggerHook', interval=50), )
train_dataloader = dict(
batch_size=64,
num_workers=24,

8
configs/textrecog/master/README.md
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@ -35,10 +35,10 @@ Attention-based scene text recognizers have gained huge success, which leverages
## Results and Models
| Methods | Backbone | | Regular Text | | | | Irregular Text | | download |
| :----------------------------------------------------------------: | :-----------: | :----: | :----------: | :----: | :-: | :----: | :------------: | :----: | :------------------------------------------------------------------: |
| | | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
| [MASTER](/configs/textrecog/master/master_resnet31_12e_st_mj_sa.py) | R31-GCAModule | 0.9490 | 0.8967 | 0.9517 | | 0.7631 | 0.8465 | 0.8854 | [model](https://download.openmmlab.com/mmocr/textrecog/master/master_resnet31_12e_st_mj_sa/master_resnet31_12e_st_mj_sa_20220915_152443-f4a5cabc.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/master/master_resnet31_12e_st_mj_sa/20220915_152443.log) |
| Methods | Backbone | | Regular Text | | | | Irregular Text | | download |
| :-------------------------------------------------------------: | :-----------: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :---------------------------------------------------------------: |
| | | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | |
| [MASTER](/configs/textrecog/master/master_resnet31_12e_st_mj_sa.py) | R31-GCAModule | 0.9490 | 0.8887 | 0.9517 | | 0.7650 | 0.8465 | 0.8889 | [model](https://download.openmmlab.com/mmocr/textrecog/master/master_resnet31_12e_st_mj_sa/master_resnet31_12e_st_mj_sa_20220915_152443-f4a5cabc.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/master/master_resnet31_12e_st_mj_sa/20220915_152443.log) |
## Citation

12
configs/textrecog/nrtr/README.md
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@ -34,12 +34,12 @@ Scene text recognition has attracted a great many researches due to its importan
## Results and Models
| Methods | Backbone | | Regular Text | | | | Irregular Text | | download |
| :------------------------------------------------------------: | :-------------------: | :----: | :----------: | :----: | :-: | :----: | :------------: | :----: | :--------------------------------------------------------------: |
| | | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
| [NRTR](/configs/textrecog/nrtr/nrtr_modality-transform_6e_st_mj.py) | NRTRModalityTransform | 0.9150 | 0.8825 | 0.9369 | | 0.7232 | 0.7783 | 0.7500 | [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_modality-transform_6e_st_mj/nrtr_modality-transform_6e_st_mj_20220916_103322-bd9425be.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_modality-transform_6e_st_mj/20220916_103322.log) |
| [NRTR](/configs/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj.py) | R31-1/8-1/4 | 0.9483 | 0.8825 | 0.9507 | | 0.7559 | 0.8016 | 0.8889 | [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj/nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322-a6a2a123.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj/20220916_103322.log) |
| [NRTR](/configs/textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj.py) | R31-1/16-1/8 | 0.9470 | 0.8964 | 0.9399 | | 0.7357 | 0.7969 | 0.8854 | [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj/nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358-43767036.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj/20220920_143358.log) |
| Methods | Backbone | | Regular Text | | | | Irregular Text | | download |
| :---------------------------------------------------------: | :-------------------: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :-----------------------------------------------------------: |
| | | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | |
| [NRTR](/configs/textrecog/nrtr/nrtr_modality-transform_6e_st_mj.py) | NRTRModalityTransform | 0.9147 | 0.8841 | 0.9369 | | 0.7246 | 0.7783 | 0.7500 | [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_modality-transform_6e_st_mj/nrtr_modality-transform_6e_st_mj_20220916_103322-bd9425be.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_modality-transform_6e_st_mj/20220916_103322.log) |
| [NRTR](/configs/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj.py) | R31-1/8-1/4 | 0.9483 | 0.8918 | 0.9507 | | 0.7578 | 0.8016 | 0.8889 | [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj/nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322-a6a2a123.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj/20220916_103322.log) |
| [NRTR](/configs/textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj.py) | R31-1/16-1/8 | 0.9470 | 0.8918 | 0.9399 | | 0.7376 | 0.7969 | 0.8854 | [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj/nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358-43767036.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj/20220920_143358.log) |
## Citation

8
configs/textrecog/robust_scanner/README.md
View File

@ -40,10 +40,10 @@ The attention-based encoder-decoder framework has recently achieved impressive r
## Results and Models
| Methods | GPUs | | Regular Text | | | | Irregular Text | | download |
| :---------------------------------------------------------------------: | :--: | :----: | :----------: | :----: | :-: | :----: | :------------: | :----: | :----------------------------------------------------------------------: |
| | | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
| [RobustScanner](/configs/textrecog/robust_scanner/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real.py) | 4 | 0.9510 | 0.8934 | 0.9320 | | 0.7559 | 0.8078 | 0.8715 | [model](https://download.openmmlab.com/mmocr/textrecog/robust_scanner/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real_20220915_152447-7fc35929.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/robust_scanner/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real/20220915_152447.log) |
| Methods | GPUs | | Regular Text | | | | Irregular Text | | download |
| :------------------------------------------------------------------: | :--: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :-------------------------------------------------------------------: |
| | | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | |
| [RobustScanner](/configs/textrecog/robust_scanner/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real.py) | 4 | 0.9510 | 0.9011 | 0.9320 | | 0.7578 | 0.8078 | 0.8750 | [model](https://download.openmmlab.com/mmocr/textrecog/robust_scanner/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real_20220915_152447-7fc35929.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/robust_scanner/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real/20220915_152447.log) |
## References

10
configs/textrecog/sar/README.md
View File

@ -40,11 +40,11 @@ Recognizing irregular text in natural scene images is challenging due to the lar
## Results and Models
| Methods | Backbone | Decoder | | Regular Text | | | | Irregular Text | | download |
| :-------------------------------------------------------: | :---------: | :------------------: | :----: | :----------: | :----: | :-: | :----: | :------------: | :----: | :---------------------------------------------------------: |
| | | | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
| [SAR](/configs/textrecog/sar/sar_r31_parallel_decoder_academic.py) | R31-1/8-1/4 | ParallelSARDecoder | 0.9533 | 0.8841 | 0.9369 | | 0.7602 | 0.8326 | 0.9028 | [model](https://download.openmmlab.com/mmocr/textrecog/sar/sar_resnet31_parallel-decoder_5e_st-sub_mj-sub_sa_real/sar_resnet31_parallel-decoder_5e_st-sub_mj-sub_sa_real_20220915_171910-04eb4e75.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/sar/sar_resnet31_parallel-decoder_5e_st-sub_mj-sub_sa_real/20220915_171910.log) |
| [SAR](/configs/textrecog/sar/sar_r31_sequential_decoder_academic.py) | R31-1/8-1/4 | SequentialSARDecoder | 0.9553 | 0.8717 | 0.9409 | | 0.7737 | 0.8093 | 0.8924 | [model](https://download.openmmlab.com/mmocr/textrecog/sar/sar_resnet31_sequential-decoder_5e_st-sub_mj-sub_sa_real/sar_resnet31_sequential-decoder_5e_st-sub_mj-sub_sa_real_20220915_185451-1fd6b1fc.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/sar/sar_resnet31_sequential-decoder_5e_st-sub_mj-sub_sa_real/20220915_185451.log) |
| Methods | Backbone | Decoder | | Regular Text | | | | Irregular Text | | download |
| :----------------------------------------------------: | :---------: | :------------------: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :------------------------------------------------------: |
| | | | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | |
| [SAR](/configs/textrecog/sar/sar_r31_parallel_decoder_academic.py) | R31-1/8-1/4 | ParallelSARDecoder | 0.9533 | 0.8964 | 0.9369 | | 0.7602 | 0.8326 | 0.9062 | [model](https://download.openmmlab.com/mmocr/textrecog/sar/sar_resnet31_parallel-decoder_5e_st-sub_mj-sub_sa_real/sar_resnet31_parallel-decoder_5e_st-sub_mj-sub_sa_real_20220915_171910-04eb4e75.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/sar/sar_resnet31_parallel-decoder_5e_st-sub_mj-sub_sa_real/20220915_171910.log) |
| [SAR](/configs/textrecog/sar/sar_r31_sequential_decoder_academic.py) | R31-1/8-1/4 | SequentialSARDecoder | 0.9553 | 0.9073 | 0.9409 | | 0.7761 | 0.8093 | 0.8958 | [model](https://download.openmmlab.com/mmocr/textrecog/sar/sar_resnet31_sequential-decoder_5e_st-sub_mj-sub_sa_real/sar_resnet31_sequential-decoder_5e_st-sub_mj-sub_sa_real_20220915_185451-1fd6b1fc.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/sar/sar_resnet31_sequential-decoder_5e_st-sub_mj-sub_sa_real/20220915_185451.log) |
## Citation

10
configs/textrecog/satrn/README.md
View File

@ -34,11 +34,11 @@ Scene text recognition (STR) is the task of recognizing character sequences in n
## Results and Models
| Methods | | Regular Text | | | | Irregular Text | | download |
| :---------------------------------------------------------------------: | :----: | :----------: | :----: | :-: | :----: | :------------: | :----: | :--------------------------------------------------------------------------: |
| | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
| [Satrn](/configs/textrecog/satrn/satrn_shallow_5e_st_mj.py) | 0.9600 | 0.9196 | 0.9606 | | 0.8031 | 0.8837 | 0.8993 | [model](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_shallow_5e_st_mj/satrn_shallow_5e_st_mj_20220915_152443-5fd04a4c.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_shallow_5e_st_mj/20220915_152443.log) |
| [Satrn_small](/configs/textrecog/satrn/satrn_shallow-small_5e_st_mj.py) | 0.9423 | 0.8995 | 0.9567 | | 0.7877 | 0.8574 | 0.8507 | [model](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_shallow-small_5e_st_mj/satrn_shallow-small_5e_st_mj_20220915_152442-5591bf27.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_shallow-small_5e_st_mj/20220915_152442.log) |
| Methods | | Regular Text | | | | Irregular Text | | download |
| :--------------------------------------------------------------------: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :---------------------------------------------------------------------: |
| | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | |
| [Satrn](/configs/textrecog/satrn/satrn_shallow_5e_st_mj.py) | 0.9600 | 0.9181 | 0.9606 | | 0.8045 | 0.8837 | 0.8993 | [model](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_shallow_5e_st_mj/satrn_shallow_5e_st_mj_20220915_152443-5fd04a4c.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_shallow_5e_st_mj/20220915_152443.log) |
| [Satrn_small](/configs/textrecog/satrn/satrn_shallow-small_5e_st_mj.py) | 0.9423 | 0.9011 | 0.9567 | | 0.7886 | 0.8574 | 0.8472 | [model](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_shallow-small_5e_st_mj/satrn_shallow-small_5e_st_mj_20220915_152442-5591bf27.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_shallow-small_5e_st_mj/20220915_152442.log) |
## Citation