TEXT DETECTION

This section uses the icdar2015 dataset as an example to introduce the training, evaluation, and testing of the detection model in PaddleOCR.

DATA PREPARATION

The icdar2015 dataset can be obtained from official website. Registration is required for downloading.

Decompress the downloaded dataset to the working directory, assuming it is decompressed under PaddleOCR/train_data/. In addition, PaddleOCR organizes many scattered annotation files into two separate annotation files for train and test respectively, which can be downloaded by wget:

# Under the PaddleOCR path
cd PaddleOCR/
wget -P ./train_data/  https://paddleocr.bj.bcebos.com/dataset/train_icdar2015_label.txt
wget -P ./train_data/  https://paddleocr.bj.bcebos.com/dataset/test_icdar2015_label.txt

After decompressing the data set and downloading the annotation file, PaddleOCR/train_data/ has two folders and two files, which are:

/PaddleOCR/train_data/icdar2015/text_localization/
  └─ icdar_c4_train_imgs/         Training data of icdar dataset
  └─ ch4_test_images/             Testing data of icdar dataset
  └─ train_icdar2015_label.txt    Training annotation of icdar dataset
  └─ test_icdar2015_label.txt     Test annotation of icdar dataset

The provided annotation file format is as follow, seperated by "\t":

" Image file name             Image annotation information encoded by json.dumps"
ch4_test_images/img_61.jpg    [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]]}, {...}]

The image annotation after json.dumps() encoding is a list containing multiple dictionaries.

The points in the dictionary represent the coordinates (x, y) of the four points of the text box, arranged clockwise from the point at the upper left corner.

transcription represents the text of the current text box. When its content is "###" it means that the text box is invalid and will be skipped during training.

If you want to train PaddleOCR on other datasets, please build the annotation file according to the above format.

TRAINING

First download the pretrained model. The detection model of PaddleOCR currently supports 3 backbones, namely MobileNetV3, ResNet18_vd and ResNet50_vd. You can use the model in PaddleClas to replace backbone according to your needs.

cd PaddleOCR/
# Download the pre-trained model of MobileNetV3
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar
# or, download the pre-trained model of ResNet18_vd
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_vd_pretrained.tar
# or, download the pre-trained model of ResNet50_vd
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar

# decompressing the pre-training model file, take MobileNetV3 as an example
tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models/

# Note: After decompressing the backbone pre-training weight file correctly, the file list in the folder is as follows:
./pretrain_models/MobileNetV3_large_x0_5_pretrained/
  └─ conv_last_bn_mean
  └─ conv_last_bn_offset
  └─ conv_last_bn_scale
  └─ conv_last_bn_variance
  └─ ......

START TRAINING

If CPU version installed, please set the parameter use_gpu to false in the configuration.

python3 tools/train.py -c configs/det/det_mv3_db.yml

In the above instruction, use -c to select the training to use the configs/det/det_db_mv3.yml configuration file. For a detailed explanation of the configuration file, please refer to config.

You can also use -o to change the training parameters without modifying the yml file. For example, adjust the training learning rate to 0.0001

# single GPU training
python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001

# multi-GPU training
# Set the GPU ID used by the '--gpus' parameter.
python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001

load trained model and continue training

If you expect to load trained model and continue the training again, you can specify the parameter Global.checkpoints as the model path to be loaded.

For example:

python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./your/trained/model

Note: The priority of Global.checkpoints is higher than that of Global.pretrain_weights, that is, when two parameters are specified at the same time, the model specified by Global.checkpoints will be loaded first. If the model path specified by Global.checkpoints is wrong, the one specified by Global.pretrain_weights will be loaded.

EVALUATION

PaddleOCR calculates three indicators for evaluating performance of OCR detection task: Precision, Recall, and Hmean.

Run the following code to calculate the evaluation indicators. The result will be saved in the test result file specified by save_res_path in the configuration file det_db_mv3.yml

When evaluating, set post-processing parameters box_thresh=0.6, unclip_ratio=1.5. If you use different datasets, different models for training, these two parameters should be adjusted for better result.

The model parameters during training are saved in the Global.save_model_dir directory by default. When evaluating indicators, you need to set Global.checkpoints to point to the saved parameter file.

python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5

Note: box_thresh and unclip_ratio are parameters required for DB post-processing, and not need to be set when evaluating the EAST model.

TEST

Test the detection result on a single image:

python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false

When testing the DB model, adjust the post-processing threshold:

python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5

Test the detection result on all images in the folder:

python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false

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