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Changelog

v0.4.1 (27/01/2022)

Highlights

  1. Visualizing edge weights in OpenSet KIE is now supported! https://github.com/open-mmlab/mmocr/pull/677
  2. Some configurations have been optimized to significantly speed up the training and testing processes! Don't worry - you can still tune these parameters in case these modifications do not work. https://github.com/open-mmlab/mmocr/pull/757
  3. Now you can use CPU to train/debug your model! https://github.com/open-mmlab/mmocr/pull/752
  4. We have fixed a severe bug that causes users unable to call mmocr.apis.test with our pre-built wheels. https://github.com/open-mmlab/mmocr/pull/667

New Features & Enhancements

Bug Fixes

Docs

New Contributors

Full Changelog: https://github.com/open-mmlab/mmocr/compare/v0.4.0...v0.4.1

v0.4.0 (15/12/2021)

Highlights

  1. We release a new text recognition model - ABINet (CVPR 2021, Oral). With it dedicated model design and useful data augmentation transforms, ABINet can achieve the best performance on irregular text recognition tasks. Check it out!
  2. We are also working hard to fulfill the requests from our community. OpenSet KIE is one of the achievement, which extends the application of SDMGR from text node classification to node-pair relation extraction. We also provide a demo script to convert WildReceipt to open set domain, though it cannot take the full advantage of OpenSet format. For more information, please read our tutorial.
  3. APIs of models can be exposed through TorchServe. Docs

Breaking Changes & Migration Guide

Postprocessor

Some refactoring processes are still going on. For all text detection models, we unified their decode implementations into a new module category, POSTPROCESSOR, which is responsible for decoding different raw outputs into boundary instances. In all text detection configs, the text_repr_type argument in bbox_head is deprecated and will be removed in the future release.

Migration Guide: Find a similar line from detection model's config:

text_repr_type=xxx,

And replace it with

postprocessor=dict(type='{MODEL_NAME}Postprocessor', text_repr_type=xxx)),

Take a snippet of PANet's config as an example. Before the change, its config for bbox_head looks like:

    bbox_head=dict(
        type='PANHead',
        text_repr_type='poly',
        in_channels=[128, 128, 128, 128],
        out_channels=6,
        loss=dict(type='PANLoss')),

Afterwards:

    bbox_head=dict(
    type='PANHead',
    in_channels=[128, 128, 128, 128],
    out_channels=6,
    loss=dict(type='PANLoss'),
    postprocessor=dict(type='PANPostprocessor', text_repr_type='poly')),

There are other postprocessors and each takes different arguments. Interested users can find their interfaces or implementations in mmocr/models/textdet/postprocess or through our api docs.

New Config Structure

We reorganized the configs/ directory by extracting reusable sections into configs/_base_. Now the directory tree of configs/_base_ is organized as follows:

_base_
├── det_datasets
├── det_models
├── det_pipelines
├── recog_datasets
├── recog_models
├── recog_pipelines
└── schedules

Most of model configs are making full use of base configs now, which makes the overall structural clearer and facilitates fair comparison across models. Despite the seemingly significant hierarchical difference, these changes would not break the backward compatibility as the names of model configs remain the same.

New Features

Refactoring

Docs

Enhancements

Bug Fixes

New Contributors

Full Changelog: https://github.com/open-mmlab/mmocr/compare/v0.3.0...0.4.0

v0.3.0 (25/8/2021)

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!

v0.2.1 (20/7/2021)

Highlights

  1. Upgrade to use MMCV-full >= 1.3.8 and MMDetection >= 2.13.0 for latest features
  2. Add ONNX and TensorRT export tool, supporting the deployment of DBNet, PSENet, PANet and CRNN (experimental) #278, #291, #300, #328
  3. Unified parameter initialization method which uses init_cfg in config files #365

New Features

  • Support TextOCR dataset #293
  • Support Total-Text dataset #266, #273, #357
  • Support grouping text detection box into lines #290, #304
  • Add benchmark_processing script that benchmarks data loading process #261
  • Add SynthText preprocessor for text recognition models #351, #361
  • Support batch inference during testing #310
  • Add user-friendly OCR inference script #366

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
  • Fix NRTR config #356, #370

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 part of Chinese documentations #353, #362
  • 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

  1. Add the NER approach Bert-softmax (NAACL'2019)
  2. Add the text detection method DRRG (CVPR'2020)
  3. Add the text detection method FCENet (CVPR'2021)
  4. Increase the ease of use via adding text detection and recognition end-to-end demo, and colab online demo.
  5. Simplify the installation.

New Features

Bug Fixes

  • Fix the duplicated point bug due to transform for textsnake #130
  • Fix CTC loss NaN #159
  • Fix error raised if result is empty in demo #144
  • Fix results missing if one image has a large number of boxes #98
  • Fix package missing in dockerfile #109

Improvements

  • Simplify installation procedure via removing compiling #188
  • Speed up panet post processing so that it can detect dense texts #188
  • Add zh-CN README #70 #95
  • Support windows #89
  • Add Colab #147 #199
  • Add 1-step installation using conda environment #193 #194 #195

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 methods SAR, Robustscanner; Segmentation based method SegOCR; Transformer based method NRTR.
  • For key information extraction, support GCN based method SDMG-R.
  • Provide checkpoints and log files for all of the methods above.