[MMOCR](https://github.com/open-mmlab/mmocr/tree/1.x) aka `mmocr` is an open-source toolbox based on PyTorch and mmdetection for text detection, text recognition, and the corresponding downstream tasks including key information extraction. It is a part of the [OpenMMLab](https://openmmlab.com/) project.
If neither **I** nor **II** meets your requirements, [building mmdeploy from source](../01-how-to-build/build_from_source.md) is the last option.
## Convert model
You can use [tools/deploy.py](https://github.com/open-mmlab/mmdeploy/blob/dev-1.x/tools/deploy.py) to convert mmocr models to the specified backend models. Its detailed usage can be learned from [here](https://github.com/open-mmlab/mmdeploy/blob/master/docs/en/02-how-to-run/convert_model.md#usage).
When using `tools/deploy.py`, it is crucial to specify the correct deployment config. We've already provided builtin deployment config [files](https://github.com/open-mmlab/mmdeploy/tree/dev-1.x/configs/mmocr) of all supported backends for mmocr, under which the config file path follows the pattern:
MMDeploy supports models of two tasks of mmocr, one is `text detection` and the other is `text-recogntion`.
**DO REMEMBER TO USE** the corresponding deployment config file when trying to convert models of different tasks.
- **{backend}:** inference backend, such as onnxruntime, tensorrt, pplnn, ncnn, openvino, coreml etc.
- **{precision}:** fp16, int8. When it's empty, it means fp32
- **{static | dynamic}:** static shape or dynamic shape
- **{shape}:** input shape or shape range of a model
In the next two chapters, we will task `dbnet` model from `text detection` task and `crnn` model from `text recognition` task respectively as examples, showing how to convert them to onnx model that can be inferred by ONNX Runtime.
You can also convert the above models to other backend models by changing the deployment config file `*_onnxruntime_dynamic.py` to [others](https://github.com/open-mmlab/mmdeploy/tree/dev-1.x/configs/mmocr), e.g., converting `dbnet` to tensorrt-fp32 model by `text-detection/text-detection_tensorrt-_dynamic-320x320-2240x2240.py`.
```{tip}
When converting mmocr models to tensorrt models, --device should be set to "cuda"
```
## Model specification
Before moving on to model inference chapter, let's know more about the converted model structure which is very important for model inference.
The converted model locates in the working directory like `mmdeploy_models/mmocr/dbnet/ort` in the previous example. It includes:
- **end2end.onnx**: backend model which can be inferred by ONNX Runtime
- \***.json**: the necessary information for mmdeploy SDK
The whole package **mmdeploy_models/mmocr/dbnet/ort** is defined as **mmdeploy SDK model**, i.e., **mmdeploy SDK model** includes both backend model and inference meta information.
## Model Inference
### Backend model inference
Take the previous converted `end2end.onnx` mode of `dbnet` as an example, you can use the following code to inference the model and visualize the results.
```python
from mmdeploy.apis.utils import build_task_processor
from mmdeploy.utils import get_input_shape, load_config
Map 'deploy_cfg', 'model_cfg', 'backend_model' and 'image' to corresponding arguments in chapter [convert text recognition model](#convert-text-recognition-model), you will get the ONNX Runtime inference results of `crnn` onnx model.
### SDK model inference
Given the above SDK models of `dbnet` and `crnn`, you can also perform SDK model inference like following,
Besides python API, mmdeploy SDK also provides other FFI (Foreign Function Interface), such as C, C++, C#, Java and so on. You can learn their usage from [demos](https://github.com/open-mmlab/mmdeploy/tree/dev-1.x/demo).
