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+---
+comments: true
+---
+
+# Text Direction Classification
+
+## 1. Method Introduction
+
+The angle classification is used in the scene where the image is not 0 degrees. In this scene, it is necessary to perform a correction operation on the text line detected in the picture. In the PaddleOCR system,
+The text line image obtained after text detection is sent to the recognition model after affine transformation. At this time, only a 0 and 180 degree angle classification of the text is required, so the built-in PaddleOCR text angle classifier **only supports 0 and 180 degree classification**. If you want to support more angles, you can modify the algorithm yourself to support.
+
+Example of 0 and 180 degree data samples：
+
+![img](./images/angle_class_example.jpg)
+
+## 2. Data Preparation
+
+Please organize the dataset as follows:
+
+The default storage path for training data is `PaddleOCR/train_data/cls`, if you already have a dataset on your disk, just create a soft link to the dataset directory:
+
+```bash linenums="1"
+ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/cls/dataset
+```
+
+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 (cls_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
+
+0 and 180 indicate that the angle of the image is 0 degrees and 180 degrees, respectively.
+
+```text linenums="1"
+" Image file name           Image annotation "
+
+train/word_001.jpg   0
+train/word_002.jpg   180
+```
+
+The final training set should have the following file structure:
+
+```text linenums="1"
+|-train_data
+    |-cls
+        |- cls_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 cls_gt_test.txt. The structure of the test set is as follows:
+
+```text linenums="1"
+|-train_data
+    |-cls
+        |- cls_gt_test.txt
+        |- test
+            |- word_001.jpg
+            |- word_002.jpg
+            |- word_003.jpg
+            | ...
+```
+
+## 3. Training
+
+Write the prepared txt file and image folder path into the configuration file under the `Train/Eval.dataset.label_file_list` and `Train/Eval.dataset.data_dir` fields, the absolute path of the image consists of the `Train/Eval.dataset.data_dir` field and the image name recorded in the txt file.
+
+PaddleOCR provides training scripts, evaluation scripts, and prediction scripts.
+
+### Start training
+
+```bash linenums="1"
+# Set PYTHONPATH path
+export PYTHONPATH=$PYTHONPATH:.
+# GPU training Support single card and multi-card training, specify the card number through --gpus.
+# Start training, the following command has been written into the train.sh file, just modify the configuration file path in the file
+python3 -m paddle.distributed.launch --gpus '0,1,2,3,4,5,6,7'  tools/train.py -c configs/cls/cls_mv3.yml
+```
+
+### Data Augmentation
+
+PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, Please uncomment the `RecAug` and `RandAugment` fields under `Train.dataset.transforms` in the configuration file.
+
+The default perturbation methods are: cvtColor, blur, jitter, Gauss noise, random crop, perspective, color reverse, RandAugment.
+
+Except for RandAugment, each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to:
+[rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py)
+[randaugment.py](../../ppocr/data/imaug/randaugment.py)
+
+### Training
+
+PaddleOCR supports alternating training and evaluation. You can modify `eval_batch_step` in `configs/cls/cls_mv3.yml` to set the evaluation frequency. By default, it is evaluated every 1000 iter. The following content will be saved during training:
+
+```bash linenums="1"
+├── best_accuracy.pdopt # Optimizer parameters for the best model
+├── best_accuracy.pdparams # Parameters of the best model
+├── best_accuracy.states # Metric info and epochs of the best model
+├── config.yml # Configuration file for this experiment
+├── latest.pdopt # Optimizer parameters for the latest model
+├── latest.pdparams # Parameters of the latest model
+├── latest.states # Metric info and epochs of the latest model
+└── train.log # Training log
+```
+
+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.
+
+**Note that the configuration file for prediction/evaluation must be consistent with the training.**
+
+## 4. Evaluation
+
+The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/cls/cls_mv3.yml` file.
+
+```bash linenums="1"
+export CUDA_VISIBLE_DEVICES=0
+# GPU evaluation, Global.checkpoints is the weight to be tested
+python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy
+```
+
+## 5. Prediction
+
+### Training engine prediction
+
+Using the model trained by paddleocr, you can quickly get prediction through the following script.
+
+Use `Global.infer_img` to specify the path of the predicted picture or folder, and use `Global.checkpoints` to specify the weight:
+
+```bash linenums="1"
+# Predict English results
+python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words_en/word_10.png
+```
+
+Input image:
+
+![](./images/word_1-20240704092713071.jpg)
+
+Get the prediction result of the input image:
+
+```bash linenums="1"
+infer_img: doc/imgs_words_en/word_10.png
+     result: ('0', 0.9999995)
+```