# Dataset Preparation ## Introduction After decades of development, the OCR community has produced a series of related datasets that often provide annotations of text in a variety of styles, making it necessary for users to convert these datasets to the required format when using them. MMOCR supports dozens of commonly used text-related datasets and provides detailed tutorials for downloading and preparing the data. In addition, we provide data conversion scripts to help users convert the annotations of widely-used OCR datasets to MMOCR formats. - [Detection Dataset Preparation](./data_prepare/det.md) - [Recognition Dataset Preparation](./data_prepare/recog.md) - [Key Information Extraction Dataset Preparation](./data_prepare/kie.md) In the following, we provide a brief overview of the data formats defined in MMOCR for each task. - As shown in the following code block, the text detection task uses the data format `TextDetDataset`, which holds the bounding box annotations, file names, and other information required for the text detection task. We provide a sample annotation file in the `tests/data/det_toy_dataset/instances_test.json` path. ```json { "metainfo": { "dataset_type": "TextDetDataset", "task_name": "textdet", "category": [{"id": 0, "name": "text"}] }, "data_list": [ { "img_path": "test_img.jpg", "height": 640, "width": 640, "instances": [ { "polygon": [0, 0, 0, 10, 10, 20, 20, 0], "bbox": [0, 0, 10, 20], "bbox_label": 0, "ignore": false, }, ], //... } ] } ``` - As shown in the following code block, the text recognition task uses the data format `TextRecogDataset`, which holds information such as text instances and image paths required by the text recognition task. An example annotation file is provided in the `tests/data/rec_toy_dataset/labels.json` path. ```json { "metainfo": { "dataset_type": "TextRecogDataset", "task_name": "textrecog", }, "data_list": [ { "img_path": "test_img.jpg", "instances": [ { "text": "GRAND" } ] } ] } ``` ## Downloading Datasets and Format Conversion As an example of the data preparation steps, you can perform the following steps to prepare the ICDAR 2015 dataset for text detection task. - Download the ICDAR 2015 dataset from the [official ICDAR website](https://rrc.cvc.uab.es/?ch=4&com=downloads). Extract the training set `ch4_training_word_images_gt.zip` and the test set zip `ch4_test_word_images_gt.zip` to the path `data/icdar2015` respectively. ```bash # Downloading datasets mkdir data/det/icdar2015 && cd data/det/icdar2015 wget https://rrc.cvc.uab.es/downloads/ch4_training_images.zip --no-check-certificate wget https://rrc.cvc.uab.es/downloads/ch4_training_localization_transcription_gt.zip --no-check-certificate wget https://rrc.cvc.uab.es/downloads/ch4_test_images.zip --no-check-certificate wget https://rrc.cvc.uab.es/downloads/Challenge4_Test_Task1_GT.zip --no-check-certificate # Extracting the zips mkdir imgs && mkdir annotations unzip ch4_training_images.zip -d imgs/training unzip ch4_training_localization_transcription_gt.zip -d annotations/training unzip ch4_test_images.zip -d imgs/test unzip Challenge4_Test_Task1_GT.zip -d annotations/test ``` - Using the scripts provided by us to convert the annotations to MMOCR supported formats. ```bash python tools/dataset_converters/textdet/icdar_converter.py data/det/icdar15/ -o data/det/icdar15/ --split-list training test -d icdar2015 ``` - After completing the above steps, the annotation format has been converted, and the file directory structure is as follows ```text data/det/icdar2015/ ├── annotations │ ├── test │ └── training ├── imgs │ ├── test │ └── training ├── instances_test.json └── instances_training.json ``` ## Dataset Configuration ### Single Dataset Training When training or evaluating a model on new datasets, we need to write the dataset config where the image path, annotation path, and image prefix are set. The path `configs/xxx/_base_/datasets/` is pre-configured with the commonly used datasets in MMOCR, here we take the ICDAR 2015 dataset as an example (see `configs/_base_/det_datasets/icdar2015.py`). ```Python ic15_det_data_root = 'data/det/icdar2015' # dataset root path # Train set config ic15_det_train = dict( type='OCRDataset', data_root=ic15_det_data_root, # dataset root path ann_file='instances_training.json', # name of annotation data_prefix=dict(img_path='imgs/'), # prefix of image path filter_cfg=dict(filter_empty_gt=True, min_size=32), # filtering empty images pipeline=None) # Test set config ic15_det_test = dict( type='OCRDataset', data_root=ic15_det_data_root, ann_file='instances_test.json', data_prefix=dict(img_path='imgs/'), test_mode=True, pipeline=None) ``` After configuring the dataset, we can import it in the corresponding model configs. For example, to train the "DBNet_R18" model on the ICDAR 2015 dataset. ```Python _base_ = [ '_base_dbnet_r18_fpnc.py', '../_base_/datasets/icdar2015.py', # import the dataset config '../_base_/default_runtime.py', '../_base_/schedules/schedule_sgd_1200e.py', ] ic15_det_train = _base_.ic15_det_train # specify the training set ic15_det_train.pipeline = _base_.train_pipeline # specify the training pipeline ic15_det_test = _base_.ic15_det_test # specify the testing set ic15_det_test.pipeline = _base_.test_pipeline # specify the testing pipeline train_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=ic15_det_train) # specify the dataset in train_dataloader val_dataloader = dict( batch_size=1, num_workers=4, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=ic15_det_test) # specify the dataset in val_dataloader test_dataloader = val_dataloader ``` ### Multi-dataset Training In addition, [`ConcatDataset`](mmocr.datasets.ConcatDataset) enables users to train or test the model on a combination of multiple datasets. You just need to set the dataset type in the dataloader to `ConcatDataset` in the configuration file and specify the corresponding list of datasets. ```Python train_list = [ic11, ic13, ic15] train_dataloader = dict( dataset=dict( type='ConcatDataset', datasets=train_list, pipeline=train_pipeline)) ``` For example, the following configuration uses the MJSynth dataset for training and 6 academic datasets (CUTE80, IIIT5K, SVT, SVTP, ICDAR2013, ICDAR2015) for testing. ```Python _base_ = [ # Import all dataset configurations you want to use '../_base_/datasets/mjsynth.py', '../_base_/datasets/cute80.py', '../_base_/datasets/iiit5k.py', '../_base_/datasets/svt.py', '../_base_/datasets/svtp.py', '../_base_/datasets/icdar2013.py', '../_base_/datasets/icdar2015.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_adadelta_5e.py', '_base_crnn_mini-vgg.py', ] # List of training datasets train_list = [_base_.mj_rec_train] # List of testing datasets test_list = [ _base_.cute80_rec_test, _base_.iiit5k_rec_test, _base_.svt_rec_test, _base_.svtp_rec_test, _base_.ic13_rec_test, _base_.ic15_rec_test ] # Use ConcatDataset to combine the datasets in the list train_dataset = dict( type='ConcatDataset', datasets=train_list, pipeline=_base_.train_pipeline) test_dataset = dict( type='ConcatDataset', datasets=test_list, pipeline=_base_.test_pipeline) train_dataloader = dict( batch_size=192 * 4, num_workers=32, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=train_dataset) test_dataloader = dict( batch_size=1, num_workers=4, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=test_dataset) val_dataloader = test_dataloader ```