# Text detection

This section uses the icdar15 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](https://rrc.cvc.uab.es/?ch=4&com=downloads). 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:
```
" 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 information before 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, and this information is not needed in the text detection task.
If you want to train PaddleOCR on other datasets, you can build the annotation file according to the above format.


## Quickstart training

First download the pretrained model. The detection model of PaddleOCR currently supports two backbones, namely MobileNetV3 and ResNet50_vd. You can use the model in [PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures) 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
# Download the pre-trained model of ResNet50
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
```

**Start training**
```
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 [link](./doc/config-en.md).

You can also use the `-o` parameter to change the training parameters without modifying the yml file. For example, adjust the training learning rate to 0.0001
```
python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
```

## Evaluation Indicator

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.

```
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
```
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.

Such as:
```
python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="./output/det_db/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 detection result

Test the detection result on a single image:
```
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy"
```

When testing the DB model, adjust the post-processing threshold:
```
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy" 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 TestReader.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
```