Changelog¶
v0.3.0 (25/8/2021)¶
Highlights¶
We add a new text recognition model – SATRN! Its pretrained checkpoint achieves the best performance over other provided text recognition models. A lighter version of SATRN is also released which can obtain ~98% of the performance of the original model with only 45 MB in size. (@2793145003) #405
Improve the demo script,
ocr.py
, which supports applying end-to-end text detection, text recognition and key information extraction models on images with easy-to-use commands. Users can find its full documentation in the demo section. (@samayala22, @manjrekarom) #371, #386, #400, #374, #428Our documentation is reorganized into a clearer structure. More useful contents are on the way! #409, #454
The requirement of
Polygon3
is removed since this project is no longer maintained or distributed. We unified all its references to equivalent substitutions inshapely
instead. #448
Breaking Changes & Migration Guide¶
Upgrade version requirement of MMDetection to 2.14.0 to avoid bugs #382
MMOCR now has its own model and layer registries inherited from MMDetection’s or MMCV’s counterparts. (#436) The modified hierarchical structure of the model registries are now organized as follows.
mmcv.MODELS -> mmdet.BACKBONES -> BACKBONES
mmcv.MODELS -> mmdet.NECKS -> NECKS
mmcv.MODELS -> mmdet.ROI_EXTRACTORS -> ROI_EXTRACTORS
mmcv.MODELS -> mmdet.HEADS -> HEADS
mmcv.MODELS -> mmdet.LOSSES -> LOSSES
mmcv.MODELS -> mmdet.DETECTORS -> DETECTORS
mmcv.ACTIVATION_LAYERS -> ACTIVATION_LAYERS
mmcv.UPSAMPLE_LAYERS -> UPSAMPLE_LAYERS
To migrate your old implementation to our new backend, you need to change the import path of any registries and their corresponding builder functions (including build_detectors
) from mmdet.models.builder
to mmocr.models.builder
. If you have referred to any model or layer of MMDetection or MMCV in your model config, you need to add mmdet.
or mmcv.
prefix to its name to inform the model builder of the right namespace to work on.
Interested users may check out MMCV’s tutorial on Registry for in-depth explanations on its mechanism.
New Features¶
Bug Fixes¶
Remove depreciated key in kie_test_imgs.py #381
Fix dimension mismatch in batch testing/inference of DBNet #383
Fix the problem of dice loss which stays at 1 with an empty target given #408
Fix undesired assignment to “pretrained” in test.py #418
Skip invalid annotations in totaltext_converter #438
Add zero division handler in poly utils, remove Polygon3 #448
Improvements¶
Replace lanms-proper with lanms-neo to support installation on Windows (with special thanks to @gen-ko who has re-distributed this package!)
Support MIM #394
Add tests for PyTorch 1.9 in CI #401
Enables fullscreen layout in readthedocs #413
General documentation enhancement #395
Update version checker #427
Add copyright info #439
Update citation information #440
Contributors¶
We thank @2793145003, @samayala22, @manjrekarom, @naarkhoo, @gen-ko, @duanjiaqi, @gaotongxiao, @cuhk-hbsun, @innerlee, @wdsd641417025 for their contribution to this release!
v0.2.1 (20/7/2021)¶
Highlights¶
New Features¶
Bug Fixes¶
Fix improper class ignorance in SDMGR Loss #221
Fix potential numerical zero division error in DRRG #224
Fix installing requirements with pip and mim #242
Fix dynamic input error of DBNet #269
Fix space parsing error in LineStrParser #285
Fix textsnake decode error #264
Correct isort setup #288
Fix a bug in SDMGR config #316
Fix kie_test_img for KIE nonvisual #319
Fix metafiles #342
Fix different device problem in FCENet #334
Ignore improper tailing empty characters in annotation files #358
Docs fixes #247, #255, #265, #267, #268, #270, #276, #287, #330, #355, #367
Improvements¶
Add backend for resizeocr #244
Skip image processing pipelines in SDMGR novisual #260
Speedup DBNet #263
Update mmcv installation method in workflow #323
Add support for ConcatDataset with two workflows #348
Add list_from_file and list_to_file utils #226
Speed up sort_vertex #239
Support distributed evaluation of KIE #234
Add pretrained FCENet on IC15 #258
Support CPU for OCR demo #227
Avoid extra image pre-processing steps #375
v0.2.0 (18/5/2021)¶
Highlights¶
Add the NER approach Bert-softmax (NAACL’2019)
Add the text detection method DRRG (CVPR’2020)
Add the text detection method FCENet (CVPR’2021)
Increase the ease of use via adding text detection and recognition end-to-end demo, and colab online demo.
Simplify the installation.
New Features¶
Bug Fixes¶
Improvements¶
v0.1.0 (7/4/2021)¶
Highlights¶
MMOCR is released.
Main Features¶
Support text detection, text recognition and the corresponding downstream tasks such as key information extraction.
For text detection, support both single-step (
PSENet
,PANet
,DBNet
,TextSnake
) and two-step (MaskRCNN
) methods.For text recognition, support CTC-loss based method
CRNN
; Encoder-decoder (with attention) based methodsSAR
,Robustscanner
; Segmentation based methodSegOCR
; Transformer based methodNRTR
.For key information extraction, support GCN based method
SDMG-R
.Provide checkpoints and log files for all of the methods above.