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.
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 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.
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.