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v0.5.0

open-mmlab/mmocr

版本发布时间: 2022-03-31 17:50:19

open-mmlab/mmocr最新发布版本:v1.0.1(2023-07-04 15:11:53)

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

Stage-wise Plugins

Block-wise Plugin (Experimental)

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

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