There are 2 options to train your own recognition model:
1. Open-source approach
For the open-source approach, you can use your own data or generate your own dataset. To generate your own data, we recommend using
TextRecognitionDataGenerator. We provide an example of a dataset here.
After you have a dataset, you can train your own model by following this repository
deep-text-recognition-benchmark.
The network needs to be fully convolutional in order to predict flexible text length. Our current network is 'None-VGG-BiLSTM-CTC'.
Once you have your trained model (a .pth file), you need 2 additional files describing recognition network architecture and model configuration.
An example is provided in custom_example.zip file here.
Please do not create an issue about data generation and model training in this repository. If you have any question regarding data generation and model training, please ask in the respective repositories.
Note: We also provide our version of a training script here. It is a modified version from deep-text-recognition-benchmark.
2. Web-based approach
Jaided AI provides a web-based (paid) service for training your own model here. You can train your model on the cloud and export it for local deployment. All 3 files are downloadable once the model has finished training.
To use your own recognition model, you need the three files from the open-source or web-based approach above. These three files have to share the same name (i.e. yourmodel.pth, yourmodel.yaml, yourmodel.py) that you will then use to call your model with in the EasyOCR API.
We provide custom_example.zip
as an example. Please download, extract and place custom_example.py, custom_example.yaml in the user_network_directory (default = ~/.EasyOCR/user_network) and place custom_example.pth in model directory (default = ~/.EasyOCR/model)
Once you place all 3 files in their respective places, you can use custom_example by
specifying recog_network like this reader = easyocr.Reader(['en'], recog_network='custom_example').