PaddleOCR/recognition_en.md at 02d8d51df4b4870b9ba8912770f04193824357a1

9.4 KiB

Raw Blame History

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. Also refer to DTRB，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.

Custom dictionary

If you need to customize dic file, please add character_dict_path field in configs/rec/rec_icdar15_train.yml to point to your dictionary path. And set character_type to ch.

Add space category

If you want to support the recognition of the space category, please set the use_space_char field in the yml file to true.

Note: use_space_char only takes effect when character_type=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

Data Augmentation

PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, please set distort: true in the configuration file.

The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse.

Each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to: img_tools.py

Training

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