222 lines
8.2 KiB
Markdown
222 lines
8.2 KiB
Markdown
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## Text recognition
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### Data preparation
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PaddleOCR supports two data formats: `LMDB` is used to train public data and evaluation algorithms; `general data` is used to train your own data:
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Please organize the dataset as follows:
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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:
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```
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ln -sf <path/to/dataset> <path/to/paddle_detection>/train_data/dataset
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```
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* Dataset download
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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
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* Use your own dataset:
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If you want to use your own data for training, please refer to the following to organize your data.
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- Training set
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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.
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* 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
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```
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" Image file name Image annotation "
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train_data/train_0001.jpg 简单可依赖
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train_data/train_0002.jpg 用科技让复杂的世界更简单
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```
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PaddleOCR provides label files for training the icdar2015 dataset, which can be downloaded in the following ways:
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```
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# Training set label
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wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt
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# Test Set Label
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wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt
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```
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The final training set should have the following file structure:
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```
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|-train_data
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|-ic15_data
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|- rec_gt_train.txt
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|- train
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|- word_001.png
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|- word_002.jpg
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|- word_003.jpg
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| ...
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```
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- Test set
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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:
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```
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|-train_data
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|-ic15_data
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|- rec_gt_test.txt
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|- test
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|- word_001.jpg
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|- word_002.jpg
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|- word_003.jpg
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| ...
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```
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- Dictionary
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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.
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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:
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```
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l
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d
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a
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r
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n
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```
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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]
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`ppocr/utils/ppocr_keys_v1.txt` is a Chinese dictionary with 6623 characters.
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`ppocr/utils/ic15_dict.txt` is an English dictionary with 36 characters.
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You can use them if needed.
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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`.
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### Start training
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PaddleOCR provides training scripts, evaluation scripts, and prediction scripts. In this section, the CRNN recognition model will be used as an example:
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First download the pretrain model, you can download the trained model to finetune on the icdar2015 data:
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```
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cd PaddleOCR/
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# Download the pre-trained model of MobileNetV3
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wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar
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# Decompress model parameters
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cd pretrain_models
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tar -xf rec_mv3_none_bilstm_ctc.tar && rm -rf rec_mv3_none_bilstm_ctc.tar
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```
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Start training:
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```
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# Set PYTHONPATH path
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export PYTHONPATH=$PYTHONPATH:.
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# GPU training Support single card and multi-card training, specify the card number through CUDA_VISIBLE_DEVICES
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export CUDA_VISIBLE_DEVICES=0,1,2,3
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# Training icdar15 English data
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python3 tools/train.py -c configs/rec/rec_icdar15_train.yml
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```
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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.
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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.
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* 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:
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| Configuration file | Algorithm | backbone | trans | seq | pred |
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| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: |
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| rec_chinese_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
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| rec_icdar15_train.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc |
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| rec_mv3_none_bilstm_ctc.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc |
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| rec_mv3_none_none_ctc.yml | Rosetta | Mobilenet_v3 large 0.5 | None | None | ctc |
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| rec_mv3_tps_bilstm_ctc.yml | STARNet | Mobilenet_v3 large 0.5 | tps | BiLSTM | ctc |
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| rec_mv3_tps_bilstm_attn.yml | RARE | Mobilenet_v3 large 0.5 | tps | BiLSTM | attention |
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| rec_r34_vd_none_bilstm_ctc.yml | CRNN | Resnet34_vd | None | BiLSTM | ctc |
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| rec_r34_vd_none_none_ctc.yml | Rosetta | Resnet34_vd | None | None | ctc |
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| rec_r34_vd_tps_bilstm_attn.yml | RARE | Resnet34_vd | tps | BiLSTM | attention |
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| rec_r34_vd_tps_bilstm_ctc.yml | STARNet | Resnet34_vd | tps | BiLSTM | ctc |
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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:
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co
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Take `rec_mv3_none_none_ctc.yml` as an example:
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```
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Global:
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...
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# Modify image_shape to fit long text
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image_shape: [3, 32, 320]
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...
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# Modify character type
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character_type: ch
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# Add a custom dictionary, such as modify the dictionary, please point the path to the new dictionary
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character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt
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...
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# Modify reader type
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reader_yml: ./configs/rec/rec_chinese_reader.yml
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...
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...
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```
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**Note that the configuration file for prediction/evaluation must be consistent with the training.**
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### Evaluation
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The evaluation data set can be modified via `configs/rec/rec_icdar15_reader.yml` setting of `label_file_path` in EvalReader.
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```
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export CUDA_VISIBLE_DEVICES=0
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# GPU evaluation, Global.checkpoints is the weight to be tested
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python3 tools/eval.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
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```
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### Prediction
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* Training engine prediction
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Using the model trained by paddleocr, you can quickly get prediction through the following script.
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The default prediction picture is stored in `infer_img`, and the weight is specified via `-o Global.checkpoints`:
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```
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# Predict English results
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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
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```
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Input image:
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Get the prediction result of the input image:
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```
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infer_img: doc/imgs_words/en/word_1.png
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index: [19 24 18 23 29]
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word : joint
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```
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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:
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```
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# Predict Chinese results
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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
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```
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Input image:
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Get the prediction result of the input image:
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```
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infer_img: doc/imgs_words/ch/word_1.jpg
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index: [2092 177 312 2503]
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word : 韩国小馆
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```
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