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Changelog

0.6.0 (05/05/2022)

Highlights

  1. A new recognition algorithm MASTER has been added into MMOCR, which was the championship solution for the "ICDAR 2021 Competition on Scientific Table Image Recognition to Latex"! The model pre-trained on SynthText and MJSynth is available for testing! Credit to @JiaquanYe
  2. DBNet++ has been released now! A new Adaptive Scale Fusion module has been equipped for feature enhancement. Benefiting from this, the new model achieved 2% better h-mean score than its predecessor on the ICDAR2015 dataset.
  3. Three more dataset converters are added: LSVT, RCTW and HierText. Check the dataset zoo (Det & Recog ) to explore further information.
  4. To enhance the data storage efficiency, MMOCR now supports loading both images and labels from .lmdb format annotations for the text recognition task. To enable such a feature, the new lmdb_converter.py is ready for use to pack your cropped images and labels into an lmdb file. For a detailed tutorial, please refer to the following sections and the doc.
  5. Testing models on multiple datasets is a widely used evaluation strategy. MMOCR now supports automatically reporting mean scores when there is more than one dataset to evaluate, which enables a more convenient comparison between checkpoints. Doc
  6. Evaluation is more flexible and customizable now. For text detection tasks, you can set the score threshold range where the best results might come out. (Doc) If too many results are flooding your text recognition train log, you can trim it by specifying a subset of metrics in evaluation config. Check out the Evaluation section for details.
  7. MMOCR provides a script to convert the .json labels obtained by the popular annotation toolkit Labelme to MMOCR-supported data format. @Y-M-Y contributed a log analysis tool that helps users gain a better understanding of the entire training process. Read tutorial docs to get started.

Lmdb Dataset

Reading images or labels from files can be slow when data are excessive, e.g. on a scale of millions. Besides, in academia, most of the scene text recognition datasets are stored in lmdb format, including images and labels. To get closer to the mainstream practice and enhance the data storage efficiency, MMOCR now officially supports loading images and labels from lmdb datasets via a new pipeline LoadImageFromLMDB. This section is intended to serve as a quick walkthrough for you to master this update and apply it to facilitate your research.

Specifications

To better align with the academic community, MMOCR now requires the following specifications for lmdb datasets:

  • The parameter describing the data volume of the dataset is num-samples instead of total_number (deprecated).
  • Images and labels are stored with keys in the form of image-000000001 and label-000000001, respectively.

Usage

  1. Use existing academic lmdb datasets if they meet the specifications; or the tool provided by MMOCR to pack images & annotations into a lmdb dataset.

  • Previously, MMOCR had a function txt2lmdb (deprecated) that only supported converting labels to lmdb format. However, it is quite different from academic lmdb datasets, which usually contain both images and labels. Now MMOCR provides a new utility lmdb_converter to convert recognition datasets with both images and labels to lmdb format.

  • Say that your recognition data in MMOCR's format are organized as follows. (See an example in ocr_toy_dataset).

    # Directory structure

├──img_path
|      |—— img1.jpg
|      |—— img2.jpg
|      |—— ...
|——label.txt (or label.jsonl)

# Annotation format

label.txt:  img1.jpg HELLO
            img2.jpg WORLD
            ...

label.jsonl:    {'filename':'img1.jpg', 'text':'HELLO'}
                {'filename':'img2.jpg', 'text':'WORLD'}
                ...

  • Then pack these files up:

    python tools/data/utils/lmdb_converter.py  {PATH_TO_LABEL} {OUTPUT_PATH} --i {PATH_TO_IMAGES}

  • Check out tools.md for more details.

  1. The second step is to modify the configuration files. For example, to train CRNN on MJ and ST datasets:

  • Set parser as LineJsonParser and file_format as 'lmdb' in dataset config

    # configs/_base_/recog_datasets/ST_MJ_train.py
train1 = dict(
    type='OCRDataset',
    img_prefix=train_img_prefix1,
    ann_file=train_ann_file1,
    loader=dict(
        type='AnnFileLoader',
        repeat=1,
        file_format='lmdb',
        parser=dict(
            type='LineJsonParser',
            keys=['filename', 'text'],
        )),
    pipeline=None,
    test_mode=False)

  • Use LoadImageFromLMDB in pipeline:

    # configs/_base_/recog_pipelines/crnn_pipeline.py
train_pipeline = [
    dict(type='LoadImageFromLMDB', color_type='grayscale'),
    ...
  1. You are good to go! Start training and MMOCR will load data from your lmdb dataset.

New Features & Enhancements

Bug Fixes

Docs

New Contributors

Full Changelog: https://github.com/open-mmlab/mmocr/compare/v0.5.0...v0.6.0

0.5.0 (31/03/2022)

Highlights

  1. MMOCR now supports SPACE recognition! (What a prominent feature!) Users only need to convert the recognition annotations that contain spaces from a plain .txt file to JSON line format .jsonl, and then revise a few configurations to enable the LineJsonParser. For more information, please read our step-by-step tutorial.
  2. Tesseract is now available in MMOCR! While MMOCR is more flexible to support various downstream tasks, users might sometimes not be satisfied with DL models and would like to turn to effective legacy solutions. Therefore, we offer this option in mmocr.utils.ocr by wrapping Tesseract as a detector and/or recognizer. Users can easily create an MMOCR object by MMOCR(det=’Tesseract’, recog=’Tesseract’). Credit to @garvan2021
  3. We release data converters for 16 widely used OCR datasets, including multiple scenarios such as document, handwritten, and scene text. Now it is more convenient to generate annotation files for these datasets. Check the dataset zoo ( Det & Recog ) to explore further information.
  4. Special thanks to @EighteenSprings @BeyondYourself @yangrisheng, who had actively participated in documentation translation!

Migration Guide - ResNet

Some refactoring processes are still going on. For text recognition models, we unified the ResNet-like architectures which are used as backbones. By introducing stage-wise and block-wise plugins, the refactored ResNet is highly flexible to support existing models, like ResNet31 and ResNet45, and other future designs of ResNet variants.

Plugin

  • Plugin is a module category inherited from MMCV's implementation of PLUGIN_LAYERS, which can be inserted between each stage of ResNet or into a basicblock. You can find a simple implementation of plugin at mmocr/models/textrecog/plugins/common.py, or click the button below.

    Plugin Example 
    @PLUGIN_LAYERS.register_module()
class Maxpool2d(nn.Module):
    """A wrapper around nn.Maxpool2d().

    Args:
        kernel_size (int or tuple(int)): Kernel size for max pooling layer
        stride (int or tuple(int)): Stride for max pooling layer
        padding (int or tuple(int)): Padding for pooling layer
    """

    def __init__(self, kernel_size, stride, padding=0, **kwargs):
        super(Maxpool2d, self).__init__()
        self.model = nn.MaxPool2d(kernel_size, stride, padding)

    def forward(self, x):
        """
        Args:
            x (Tensor): Input feature map

        Returns:
            Tensor: The tensor after Maxpooling layer.
        """
        return self.model(x)

Stage-wise Plugins

  • ResNet is composed of stages, and each stage is composed of blocks. E.g., ResNet18 is composed of 4 stages, and each stage is composed of basicblocks. For each stage, we provide two ports to insert stage-wise plugins by giving plugins parameters in ResNet.

    [port1: before stage] ---> [stage] ---> [port2: after stage]

  • E.g. Using a ResNet with four stages as example. Suppose we want to insert an additional convolution layer before each stage, and an additional convolution layer at stage 1, 2, 4. Then you can define the special ResNet18 like this

    resnet18_speical = ResNet(
        # for simplicity, some required
        # parameters are omitted
        plugins=[
            dict(
                cfg=dict(
                type='ConvModule',
                kernel_size=3,
                stride=1,
                padding=1,
                norm_cfg=dict(type='BN'),
                act_cfg=dict(type='ReLU')),
                stages=(True, True, True, True),
                position='before_stage')
            dict(
                cfg=dict(
                type='ConvModule',
                kernel_size=3,
                stride=1,
                padding=1,
                norm_cfg=dict(type='BN'),
                act_cfg=dict(type='ReLU')),
                stages=(True, True, False, True),
                position='after_stage')
        ])

  • You can also insert more than one plugin in each port and those plugins will be executed in order. Let's take ResNet in MASTER as an example:

    Multiple Plugins Example

    • ResNet in Master is based on ResNet31. And after each stage, a module named GCAModule will be used. The GCAModule is inserted before the stage-wise convolution layer in ResNet31. In conlusion, there will be two plugins at after_stage port in the same time.

      resnet_master = ResNet(
                # for simplicity, some required
                # parameters are omitted
                plugins=[
                    dict(
                        cfg=dict(type='Maxpool2d', kernel_size=2, stride=(2, 2)),
                        stages=(True, True, False, False),
                        position='before_stage'),
                    dict(
                        cfg=dict(type='Maxpool2d', kernel_size=(2, 1), stride=(2, 1)),
                        stages=(False, False, True, False),
                        position='before_stage'),
                    dict(
                        cfg=dict(type='GCAModule', kernel_size=3, stride=1, padding=1),
                        stages=[True, True, True, True],
                        position='after_stage'),
                    dict(
                        cfg=dict(
                            type='ConvModule',
                            kernel_size=3,
                            stride=1,
                            padding=1,
                            norm_cfg=dict(type='BN'),
                            act_cfg=dict(type='ReLU')),
                        stages=(True, True, True, True),
                        position='after_stage')
                ])

  • In each plugin, we will pass two parameters (in_channels, out_channels) to support operations that need the information of current channels.

Block-wise Plugin (Experimental)

  • We also refactored the BasicBlock used in ResNet. Now it can be customized with block-wise plugins. Check here for more details.

  • BasicBlock is composed of two convolution layer in the main branch and a shortcut branch. We provide four ports to insert plugins.

        [port1: before_conv1] ---> [conv1] --->
    [port2: after_conv1] ---> [conv2] --->
    [port3: after_conv2] ---> +(shortcut) ---> [port4: after_shortcut]

  • In each plugin, we will pass a parameter in_channels to support operations that need the information of current channels.

  • E.g. Build a ResNet with customized BasicBlock with an additional convolution layer before conv1:

    Block-wise Plugin Example 
    resnet_31 = ResNet(
        in_channels=3,
        stem_channels=[64, 128],
        block_cfgs=dict(type='BasicBlock'),
        arch_layers=[1, 2, 5, 3],
        arch_channels=[256, 256, 512, 512],
        strides=[1, 1, 1, 1],
        plugins=[
            dict(
                cfg=dict(type='Maxpool2d',
                kernel_size=2,
                stride=(2, 2)),
                stages=(True, True, False, False),
                position='before_stage'),
            dict(
                cfg=dict(type='Maxpool2d',
                kernel_size=(2, 1),
                stride=(2, 1)),
                stages=(False, False, True, False),
                position='before_stage'),
            dict(
                cfg=dict(
                type='ConvModule',
                kernel_size=3,
                stride=1,
                padding=1,
                norm_cfg=dict(type='BN'),
                act_cfg=dict(type='ReLU')),
                stages=(True, True, True, True),
                position='after_stage')
        ])

Full Examples

ResNet without plugins

  • ResNet45 is used in ASTER and ABINet without any plugins.

    resnet45_aster = ResNet(
    in_channels=3,
    stem_channels=[64, 128],
    block_cfgs=dict(type='BasicBlock', use_conv1x1='True'),
    arch_layers=[3, 4, 6, 6, 3],
    arch_channels=[32, 64, 128, 256, 512],
    strides=[(2, 2), (2, 2), (2, 1), (2, 1), (2, 1)])

resnet45_abi = ResNet(
    in_channels=3,
    stem_channels=32,
    block_cfgs=dict(type='BasicBlock', use_conv1x1='True'),
    arch_layers=[3, 4, 6, 6, 3],
    arch_channels=[32, 64, 128, 256, 512],
    strides=[2, 1, 2, 1, 1])
ResNet with plugins

  • ResNet31 is a typical architecture to use stage-wise plugins. Before the first three stages, Maxpooling layer is used. After each stage, a convolution layer with BN and ReLU is used.

    resnet_31 = ResNet(
    in_channels=3,
    stem_channels=[64, 128],
    block_cfgs=dict(type='BasicBlock'),
    arch_layers=[1, 2, 5, 3],
    arch_channels=[256, 256, 512, 512],
    strides=[1, 1, 1, 1],
    plugins=[
        dict(
            cfg=dict(type='Maxpool2d',
            kernel_size=2,
            stride=(2, 2)),
            stages=(True, True, False, False),
            position='before_stage'),
        dict(
            cfg=dict(type='Maxpool2d',
            kernel_size=(2, 1),
            stride=(2, 1)),
            stages=(False, False, True, False),
            position='before_stage'),
        dict(
            cfg=dict(
            type='ConvModule',
            kernel_size=3,
            stride=1,
            padding=1,
            norm_cfg=dict(type='BN'),
            act_cfg=dict(type='ReLU')),
            stages=(True, True, True, True),
            position='after_stage')
    ])

Migration Guide - Dataset Annotation Loader

The annotation loaders, LmdbLoader and HardDiskLoader, are unified into AnnFileLoader for a more consistent design and wider support on different file formats and storage backends. AnnFileLoader can load the annotations from disk(default), http and petrel backend, and parse the annotation in txt or lmdb format. LmdbLoader and HardDiskLoader are deprecated, and users are recommended to modify their configs to use the new AnnFileLoader. Users can migrate their legacy loader HardDiskLoader referring to the following example:

# Legacy config
train = dict(
    type='OCRDataset',
    ...
    loader=dict(
        type='HardDiskLoader',
        ...))

# Suggested config
train = dict(
    type='OCRDataset',
    ...
    loader=dict(
        type='AnnFileLoader',
        file_storage_backend='disk',
        file_format='txt',
        ...))

Similarly, using AnnFileLoader with file_format='lmdb' instead of LmdbLoader is strongly recommended.

New Features & Enhancements

Bug Fixes

Docs

New Contributors

Full Changelog: https://github.com/open-mmlab/mmocr/compare/v0.4.1...v0.5.0

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,
        module_loss=dict(type='PANModuleLoss')),

Afterwards:

    bbox_head=dict(
    type='PANHead',
    in_channels=[128, 128, 128, 128],
    out_channels=6,
    module_loss=dict(type='PANModuleLoss'),
    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.