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Getting Started

In this guide we will show you some useful commands and familiarize you with MMOCR. We also provide a notebook that can help you get the most out of MMOCR.

Installation

Check out our installation guide for full steps.

Dataset Preparation

MMOCR supports numerous datasets which are classified by the type of their corresponding tasks. You may find their preparation steps in these sections: Detection Datasets, Recognition Datasets, KIE Datasets and NER Datasets.

Inference with Pretrained Models

You can perform end-to-end OCR on our demo image with one simple line of command:

python mmocr/utils/ocr.py demo/demo_text_ocr.jpg --print-result --imshow

Its detection result will be printed out and a new window will pop up with result visualization. More demo and full instructions can be found in Inference.

Training

Training with Toy Dataset

We provide a toy dataset under tests/data on which you can get a sense of training before the academic dataset is prepared.

For example, to train a text recognition task with seg method and toy dataset,

python tools/train.py configs/textrecog/seg/seg_r31_1by16_fpnocr_toy_dataset.py --work-dir seg

To train a text recognition task with sar method and toy dataset,

python tools/train.py configs/textrecog/sar/sar_r31_parallel_decoder_toy_dataset.py --work-dir sar

Training with Academic Dataset

Once you have prepared required academic dataset following our instruction, the only last thing to check is if the model’s config points MMOCR to the correct dataset path. Suppose we want to train DBNet on ICDAR 2015, and part of configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py looks like the following:

dataset_type = 'IcdarDataset'
data_root = 'data/icdar2015'
data = dict(
    train=dict(
        type=dataset_type,
        ann_file=data_root + '/instances_training.json',
        img_prefix=data_root + '/imgs',
        pipeline=train_pipeline)
    val=dict(
        type=dataset_type,
        ann_file=data_root + '/instances_test.json',
        img_prefix=data_root + '/imgs',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=data_root + '/instances_test.json',
        img_prefix=data_root + '/imgs',
        pipeline=test_pipeline))

You would need to check if data/icdar2015 is right. Then you can start training with the command:

python tools/train.py configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py --work-dir dbnet

You can find full training instructions, explanations and useful training configs in Training.

Testing

Suppose now you have finished the training of DBNet and the latest model has been saved in dbnet/latest.pth. You can evaluate its performance on the test set using the hmean-iou metric with the following command:

python tools/test.py configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py dbnet/latest.pth --eval hmean-iou

Evaluating any pretrained model accessible online is also allowed:

python tools/test.py configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_r18_fpnc_sbn_1200e_icdar2015_20210329-ba3ab597.pth --eval hmean-iou

More instructions on testing are available in Testing.

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