236 lines
8.5 KiB
Markdown
236 lines
8.5 KiB
Markdown
## 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_ocr>/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`.
|
||
|
||
### 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
|
||
# Whether to use data augmentation
|
||
distort: true
|
||
# Whether to recognize spaces
|
||
use_space_char: true
|
||
...
|
||
|
||
...
|
||
|
||
Optimizer:
|
||
...
|
||
# Add learning rate decay strategy
|
||
decay:
|
||
function: cosine_decay
|
||
# Each epoch contains iter number
|
||
step_each_epoch: 20
|
||
# Total epoch number
|
||
total_epoch: 1000
|
||
```
|
||
**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:
|
||
|
||
|
||
|
||
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:
|
||
|
||
|
||
|
||
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
|
||
```
|