diff --git a/docs/en/user_guides/visualization.md b/docs/en/user_guides/visualization.md
index f0fa8b81..40591be2 100644
--- a/docs/en/user_guides/visualization.md
+++ b/docs/en/user_guides/visualization.md
@@ -1 +1,107 @@
# Visualization
+
+Before reading this tutorial, it is recommended to read MMEngine's [Visualization](https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/visualization.md) documentation to get a first glimpse of the `Visualizer` definition and usage.
+
+In brief, the [`Visualizer`](mmengine.visualization.Visualizer) is implemented in MMEngine to meet the daily visualization needs, and contains three main functions:
+
+- Implement common drawing APIs, such as [`draw_bboxes`](mmengine.visualization.Visualizer.draw_bboxes) which implements bounding box drawing functions, [`draw_lines`](mmengine.visualization.Visualizer.draw_lines) implements the line drawing function.
+- Support writing visualization results, learning rate curves, loss function curves, and verification accuracy curves to various backends, including local disks and common deep learning training logging tools such as [TensorBoard](https://www.tensorflow.org/tensorboard) and [Wandb](https://wandb.ai/site).
+- Support calling anywhere in the code to visualize or record intermediate states of the model during training or testing, such as feature maps and validation results.
+
+Based on MMEngine's Visualizer, MMOCR comes with a variety of pre-built visualization tools that can be used by the user by simply modifying the following configuration files.
+
+- The `tools/analysis_tools/browse_dataset.py` script provides a dataset visualization function that draws images and corresponding annotations after Data Transforms, as described in [`browse_dataset.py`](useful_tools.md).
+- MMEngine implements `LoggerHook`, which uses `Visualizer` to write the learning rate, loss and evaluation results to the backend set by `Visualizer`. Therefore, by modifying the `Visualizer` backend in the configuration file, for example to ` TensorBoardVISBackend` or `WandbVISBackend`, you can implement logging to common training logging tools such as `TensorBoard` or `WandB`, thus making it easy for users to use these visualization tools to analyze and monitor the training process.
+- The `VisualizerHook` is implemented in MMOCR, which uses the `Visualizer` to visualize or store the prediction results of the validation or prediction phase into the backend set by the `Visualizer`, so by modifying the `Visualizer` backend in the configuration file, for example, to ` TensorBoardVISBackend` or `WandbVISBackend`, you can implement storing the predicted images to `TensorBoard` or `Wandb`.
+
+## Configuration
+
+Thanks to the use of the registration mechanism, in MMOCR we can set the behavior of the `Visualizer` by modifying the configuration file. Usually, we define the default configuration for the visualizer in `task/_base_/default_runtime.py`, see [configuration tutorial](config.md) for details.
+
+```Python
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+ type='TextxxxLocalVisualizer', # use different visualizers for different tasks
+ vis_backends=vis_backends,
+ name='visualizer')
+```
+
+Based on the above example, we can see that the configuration of `Visualizer` consists of two main parts, namely, the type of `Visualizer` and the visualization backend `vis_backends` it uses.
+
+- For different OCR tasks, various visualizers are pre-configured in MMOCR, including [`TextDetLocalVisualizer`](mmocr.visualization.TextDetLocalVisualizer), [`TextRecogLocalVisualizer`](mmocr.visualization.TextRecogLocalVisualizer), [`TextSpottingLocalVisualizer`](mmocr.visualization.TextSpottingLocalVisualizer) and [`KIELocalVisualizer`](mmocr.visualization.KIELocalVisualizer). These visualizers extend the basic Visulizer API according to the characteristics of their tasks and implement the corresponding tag information interface `add_datasamples`. For example, users can directly use `TextDetLocalVisualizer` to visualize labels or predictions for text detection tasks.
+- MMOCR sets the visualization backend `vis_backend` to the local visualization backend `LocalVisBackend` by default, saving all visualization results and other training information in a local folder.
+
+## Storage
+
+MMOCR uses the local visualization backend [`LocalVisBackend`](mmengine.visualization.LocalVisBackend) by default, and the model loss, learning rate, model evaluation accuracy and visualization The information stored in `VisualizerHook` and `LoggerHook`, including loss, learning rate, evaluation accuracy will be saved to the `{work_dir}/{config_name}/{time}/{vis_data}` folder by default. In addition, MMOCR also supports other common visualization backends, such as `TensorboardVisBackend` and `WandbVisBackend`, and you only need to change the `vis_backends` type in the configuration file to the corresponding visualization backend. For example, you can store data to `TensorBoard` and `Wandb` by simply inserting the following code block into the configuration file.
+
+```Python
+_base_.Visualizer.vis_backends = [
+ dict(type='LocalVisBackend'),
+ dict(type='TensorboardVisBackend'),
+ dict(type='WandbVisBackend'),]
+```
+
+## Plot
+
+### Plot the prediction results
+
+MMOCR mainly uses [`VisualizationHook`](mmocr.engine.hooks.VisualizationHook) to plot the prediction results of validation and test, by default `VisualizationHook` is off, and the default configuration is as follows.
+
+```Python
+visualization=dict( # user visualization of validation and test results
+ type='VisualizationHook',
+ enable=False,
+ interval=1,
+ show=False,
+ draw_gt=False,
+ draw_pred=False)
+```
+
+The following table shows the parameters supported by `VisualizationHook`.
+
+| Parameters | Description |
+| :--------: | :-----------------------------------------------------------------------------------------------------------: |
+| enable | The VisualizationHook is turned on and off by the enable parameter, which is the default state. |
+| interval | Controls how much iteration to store or display the results of a val or test if VisualizationHook is enabled. |
+| show | Controls whether to visualize the results of val or test. |
+| draw_gt | Whether the results of val or test are drawn with or without labeling information |
+| draw_pred | whether to draw predictions for val or test results |
+
+If you want to enable `VisualizationHook` related functions and configurations during training or testing, you only need to modify the configuration, take `dbnet_resnet18_fpnc_1200e_icdar2015.py` as an example, draw annotations and predictions at the same time, and display the images, the configuration can be modified as follows
+
+```Python
+visualization = _base_.default_hooks.visualization
+visualization.update(
+ dict(enable=True, show=True, draw_gt=True, draw_pred=True))
+```
+
+
+
+
+
+
+
+
+
+
+
+
+
+