recognition_en.md

## Text recognition

### Data preparation


PaddleOCR supports two data formats: `LMDB` is used to train public data and evaluation algorithms; `general data` is used to train your own data:

Please organize the dataset as follows:

The default storage path for training data is `PaddleOCR/train_data`, if you already have a dataset on your disk, just create a soft link to the dataset directory:

```
ln -sf <path/to/dataset> <path/to/paddle_detection>/train_data/dataset
```


* Dataset download

If you do not have a dataset locally, you can download it on the official website [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads). Also refer to [DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here)，download the lmdb format dataset required for benchmark

* Use your own dataset:

If you want to use your own data for training, please refer to the following to organize your data.

- Training set

First put the training images in the same folder (train_images), and use a txt file (rec_gt_train.txt) to store the image path and label.

* Note: by default, the image path and image label are split with \t, if you use other methods to split, it will cause training error

```
" Image file name           Image annotation "

train_data/train_0001.jpg   简单可依赖
train_data/train_0002.jpg   用科技让复杂的世界更简单
```
PaddleOCR provides label files for training the icdar2015 dataset, which can be downloaded in the following ways:

```
# Training set label
wget -P ./train_data/ic15_data  https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt
# Test Set Label
wget -P ./train_data/ic15_data  https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt
```

The final training set should have the following file structure:

```
|-train_data
    |-ic15_data
        |- rec_gt_train.txt
        |- train
            |- word_001.png
            |- word_002.jpg
            |- word_003.jpg
            | ...
```

- Test set

Similar to the training set, the test set also needs to be provided a folder containing all images (test) and a rec_gt_test.txt. The structure of the test set is as follows:

```
|-train_data
    |-ic15_data
        |- rec_gt_test.txt
        |- test
            |- word_001.jpg
            |- word_002.jpg
            |- word_003.jpg
            | ...
```

- Dictionary

Finally, a dictionary ({word_dict_name}.txt) needs to be provided so that when the model is trained, all the characters that appear can be mapped to the dictionary index.

Therefore, the dictionary needs to contain all the characters that you want to be recognized correctly. {word_dict_name}.txt needs to be written in the following format and saved in the `utf-8` encoding format:

```
l
d
a
d
r
n
```

In `word_dict.txt`, there is a single word in each line, which maps characters and numeric indexes together, e.g "and" will be mapped to [2 5 1]

`ppocr/utils/ppocr_keys_v1.txt` is a Chinese dictionary with 6623 characters.

`ppocr/utils/ic15_dict.txt` is an English dictionary with 36 characters.

You can use them if needed.

To customize the dict file, please modify the `character_dict_path` field in `configs/rec/rec_icdar15_train.yml` and set `character_type` to `ch`.

### Start training

PaddleOCR provides training scripts, evaluation scripts, and prediction scripts. In this section, the CRNN recognition model will be used as an example:

First download the pretrain model, you can download the trained model to finetune on the icdar2015 data:

```
cd PaddleOCR/
# Download the pre-trained model of MobileNetV3
wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar
# Decompress model parameters
cd pretrain_models
tar -xf rec_mv3_none_bilstm_ctc.tar && rm -rf rec_mv3_none_bilstm_ctc.tar
```

Start training:

```
# Set PYTHONPATH path
export PYTHONPATH=$PYTHONPATH:.
# GPU training Support single card and multi-card training, specify the card number through CUDA_VISIBLE_DEVICES
export CUDA_VISIBLE_DEVICES=0,1,2,3
# Training icdar15 English data
python3 tools/train.py -c configs/rec/rec_icdar15_train.yml
```

PaddleOCR supports alternating training and evaluation. You can modify `eval_batch_step` in `configs/rec/rec_icdar15_train.yml` to set the evaluation frequency. By default, it is evaluated every 500 iter and the best acc model is saved under `output/rec_CRNN/best_accuracy` during the evaluation process.

If the evaluation set is large, the test will be time-consuming. It is recommended to reduce the number of evaluations, or evaluate after training.

* Tip: You can use the `-c` parameter to select multiple model configurations under the `configs/rec/` path for training. The recognition algorithms supported by PaddleOCR are:


| Configuration file |  Algorithm |   backbone |   trans   |   seq      |     pred     |
| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   |
| rec_chinese_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  |
| rec_icdar15_train.yml |  CRNN |   Mobilenet_v3 large 0.5 |  None   |  BiLSTM |  ctc  |
| rec_mv3_none_bilstm_ctc.yml |  CRNN |   Mobilenet_v3 large 0.5 |  None   |  BiLSTM |  ctc  |
| rec_mv3_none_none_ctc.yml |  Rosetta |   Mobilenet_v3 large 0.5 |  None   |  None |  ctc  |
| rec_mv3_tps_bilstm_ctc.yml |  STARNet |   Mobilenet_v3 large 0.5 |  tps   |  BiLSTM |  ctc  |
| rec_mv3_tps_bilstm_attn.yml |  RARE |   Mobilenet_v3 large 0.5 |  tps   |  BiLSTM |  attention  |
| rec_r34_vd_none_bilstm_ctc.yml |  CRNN |   Resnet34_vd |  None   |  BiLSTM |  ctc  |
| rec_r34_vd_none_none_ctc.yml |  Rosetta |   Resnet34_vd |  None   |  None |  ctc  |
| rec_r34_vd_tps_bilstm_attn.yml | RARE | Resnet34_vd | tps | BiLSTM | attention |
| rec_r34_vd_tps_bilstm_ctc.yml | STARNet | Resnet34_vd | tps | BiLSTM | ctc |

For training Chinese data, it is recommended to use `rec_chinese_lite_train.yml`. If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:
co
Take `rec_mv3_none_none_ctc.yml` as an example:
```
Global:
  ...
  # Modify image_shape to fit long text
  image_shape: [3, 32, 320]
  ...
  # Modify character type
  character_type: ch
  # Add a custom dictionary, such as modify the dictionary, please point the path to the new dictionary
  character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt
  ...
  # Modify reader type
  reader_yml: ./configs/rec/rec_chinese_reader.yml
  ...

...
```
**Note that the configuration file for prediction/evaluation must be consistent with the training.**



### Evaluation

The evaluation data set can be modified via `configs/rec/rec_icdar15_reader.yml` setting of `label_file_path` in EvalReader.

```
export CUDA_VISIBLE_DEVICES=0
# GPU evaluation, Global.checkpoints is the weight to be tested
python3 tools/eval.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
```

### Prediction

* Training engine prediction

Using the model trained by paddleocr, you can quickly get prediction through the following script.

The default prediction picture is stored in `infer_img`, and the weight is specified via `-o Global.checkpoints`:

```
# Predict English results
python3 tools/infer_rec.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg
```

Input image:

![](../imgs_words/en/word_1.png)

Get the prediction result of the input image:

```
infer_img: doc/imgs_words/en/word_1.png
     index: [19 24 18 23 29]
     word : joint
```

The configuration file used for prediction must be consistent with the training. For example, you completed the training of the Chinese model with `python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml`, you can use the following command to predict the Chinese model:

```
# Predict Chinese results
python3 tools/infer_rec.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/ch/word_1.jpg
```

Input image:

![](../imgs_words/ch/word_1.jpg)

Get the prediction result of the input image:

```
infer_img: doc/imgs_words/ch/word_1.jpg
     index: [2092  177  312 2503]
     word : 韩国小馆
```