Use the Text Detection Module

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Taking an image dataset of identification numbers (download) as an example, this section will show you how to use the Text Detection module to detect the text area in an image. The Text Detection module can be followed by a Text Recognition module to output the recognized numbers, letters, or some special symbols.

You can also use your own data. The usage process is overall the same, but the labeling part is different.
  1. Create a new project and add the Text Detection module: Click New Project after you opened the software, name the project, and select a directory to save the project. Then, click example projects icon create in the upper-right corner and add the Text Detection module.

    example projects add project
  2. Import the image dataset: Extract the downloaded zip file. Select Import  Folder on the left of the interface to import the image dataset.

    example projects import images
  3. Select an ROI: Click the ROI Tool button example projects icon roi and adjust the frame to set an ROI that covers the text areas of all images. Then, click Apply on the top of the selection region to save the setting. Setting the ROI can avoid interferences from the background.

    example projects roi
  4. Split the dataset into the training set and validation set: By default, 80% of the images in the dataset will be split into the training set, and the rest 20% will be split into the validation set. You can click example projects icon slider and drag the slider to adjust the proportion. Please make sure that both the training set and validation set include all kinds of text areas to be detected. If the default training set and validation set cannot meet this requirement, please right-click the name of the image and then click Switch to training set or Switch to validation set to adjust the set to which the image belongs.

    example projects move image
  5. Label the images: Select an appropriate tool from the toolbar to label the images.

    example projects labeling
  6. Train the model: Keep the default training parameter settings and click Train to start training the model.

    example projects training chart
  7. Validate the Model: After the training is completed, click Validate to validate the model and check the results.

    example projects result verification
  8. Export the model: Click Export. Then, set Input image size in the pop-up dialog box, click Export, and select a directory to save the exported model.

    By default, the Input image size is consistent with that set in the Validation Parameters dialog box. In validation, if the inference speed and accuracy are up to standard, you can keep the default setting.
example projects model files

The exported model can be used in Mech-Vision and Mech-DLK SDK. Click here to view the details.

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