format angle_class
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## 文字角度分类
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### 方法介绍
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文字角度分类主要用于图片非0度的场景下，在这种场景下需要对图片里检测到的文本行进行一个转正的操作。在PaddleOCR系统内，
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# 文本方向分类器
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- [方法介绍](#方法介绍)
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- [数据准备](#数据准备)
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- [启动训练](#启动训练)
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- [训练](#训练)
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- [评估](#评估)
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- [预测](#预测)
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<a name="方法介绍"></a>
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## 方法介绍
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文本方向分类器主要用于图片非0度的场景下，在这种场景下需要对图片里检测到的文本行进行一个转正的操作。在PaddleOCR系统内，
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文字检测之后得到的文本行图片经过仿射变换之后送入识别模型，此时只需要对文字进行一个0和180度的角度分类，因此PaddleOCR内置的
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文字角度分类器**只支持了0和180度的分类**。如果想支持更多角度，可以自己修改算法进行支持。
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文本方向分类器**只支持了0和180度的分类**。如果想支持更多角度，可以自己修改算法进行支持。
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0和180度数据样本例子：
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### 数据准备
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<a name="数据准备"></a>
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## 数据准备
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请按如下步骤设置数据集：
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@ -59,8 +69,8 @@ train/cls/train/word_002.jpg 180
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|- word_003.jpg
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| ...
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```
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### 启动训练
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<a name="启动训练"></a>
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## 启动训练
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将准备好的txt文件和图片文件夹路径分别写入配置文件的 `Train/Eval.dataset.label_file_list` 和 `Train/Eval.dataset.data_dir` 字段下，`Train/Eval.dataset.data_dir`字段下的路径和文件里记载的图片名构成了图片的绝对路径。
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@ -88,7 +98,8 @@ PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入
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*由于OpenCV的兼容性问题，扰动操作暂时只支持linux*
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### 训练
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<a name="训练"></a>
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## 训练
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PaddleOCR支持训练和评估交替进行, 可以在 `configs/cls/cls_mv3.yml` 中修改 `eval_batch_step` 设置评估频率，默认每1000个iter评估一次。训练过程中将会保存如下内容：
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```bash
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@ -106,7 +117,8 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/cls/cls_mv3.yml`
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**注意，预测/评估时的配置文件请务必与训练一致。**
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### 评估
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<a name="评估"></a>
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## 评估
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评估数据集可以通过修改`configs/cls/cls_mv3.yml`文件里的`Eval.dataset.label_file_list` 字段设置。
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@ -116,7 +128,8 @@ export CUDA_VISIBLE_DEVICES=0
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python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy
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```
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### 预测
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<a name="预测"></a>
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## 预测
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* 训练引擎的预测
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@ -1,13 +1,22 @@
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## TEXT ANGLE CLASSIFICATION
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# TEXT ANGLE CLASSIFICATION
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### Method introduction
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- [Method introduction](#method-introduction)
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- [DATA PREPARATION](#data-preparation)
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- [TRAINING](#training)
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- [EVALUATION](#evaluation)
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- [PREDICTION](#prediction)
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<a name="method-introduction"></a>
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## Method introduction
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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,
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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.
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Example of 0 and 180 degree data samples：
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### DATA PREPARATION
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<a name="data-preparation"></a>
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## DATA PREPARATION
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Please organize the dataset as follows:
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@ -62,8 +71,8 @@ containing all images (test) and a cls_gt_test.txt. The structure of the test se
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|- word_003.jpg
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| ...
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```
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### TRAINING
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<a name="training"></a>
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## TRAINING
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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.
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PaddleOCR provides training scripts, evaluation scripts, and prediction scripts.
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@ -107,7 +116,8 @@ If the evaluation set is large, the test will be time-consuming. It is recommend
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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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<a name="evaluation"></a>
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## EVALUATION
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The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/cls/cls_mv3.yml` file.
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@ -116,8 +126,8 @@ 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/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy
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```
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### PREDICTION
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<a name="prediction"></a>
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## PREDICTION
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* Training engine prediction
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