Highlights

1. 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
2. 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, #428
3. Our documentation is reorganized into a clearer structure. More useful contents are on the way! #409, #454
4. The requirement of Polygon3 is removed since this project is no longer maintained or distributed. We unified all its references to equivalent substitutions in shapely instead. #448

Breaking Changes & Migration Guide

1. Upgrade version requirement of MMDetection to 2.14.0 to avoid bugs #382
2. 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

• Automatically replace SyncBN with BN for inference #420, #453
• Support batch inference for CRNN and SegOCR #407
• Support exporting documentation in pdf or epub format #406
• Support persistent_workers option in data loader #459

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 a wrong link in ocr.py (@naarkhoo) #417
• Fix undesired assignment to "pretrained" in test.py #418
• Fix a problem in polygon generation of DBNet #421, #443
• 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!