Information Reading (1D/2D Barcode Recognition)

Converting the image color space can transform the input image from one color space to another, for example, from BGR to grayscale, BGR to HSV, etc. Through color space conversion, image features can be better highlighted to facilitate subsequent image processing.

For detailed parameter description and tuning examples, please read Convert Color Space.

In image preprocessing, you can perform enhancement, denoising, morphological transformations, grayscale inversion, edge extraction, and other preprocessing operations on the input image.

For detailed parameter description and tuning examples, please read Image Preprocessing.

After completing the above parameter settings, you can click Run Step or Run project to view the preprocessing result.

Then, click Next to proceed to the target object recognition workflow.

After entering the pose correction workflow, you need to create a new correction parameter group. The system supports creating multiple parameter groups, each of which can independently set recognition regions and parameters without affecting each other.

Click the Add button to enter the new parameter group window. When creating a parameter group, select an appropriate correction method based on image features and configure the corresponding parameters.

Currently, the following three correction methods are supported:

After creating the parameter group, right-click the parameter group name, or directly click the function buttons on its right side to perform operations such as rename, delete, or create a copy.

2D alignment is a correction method that aligns the recognition target in the input image with the template through translation and rotation operations. It can eliminate recognition errors caused by inconsistent recognition target positions, improving recognition stability. After selecting this method, you need to complete recognition region settings, template settings, and recognition parameter tuning in sequence.

2D Blob alignment is a Blob-based correction method. This method detects all Blobs in the image and filters out the Blob with the most prominent geometric features. It then adjusts the image pose so that the target Blob’s centroid aligns with the image center point, and the principal axis of its minimum bounding rotated rectangle aligns with the image coordinate axes. After selecting 2D Blob alignment, you need to complete recognition region settings and recognition parameter tuning in sequence.

After completing the above parameter settings, click Run Step or Run project to view the running result.

Then, click Next to proceed to the error-proofing check workflow.

Under Set Target Region, click the rectangle or circle drawing button, then drag in the data visualization area to draw the target region. The target region should cover the 1D or 2D code to be recognized.

To exclude irrelevant background or interference within the target region, click the polygon drawing button under Set Mask Region, then draw the mask region in the data visualization area. To remove the mask region, click Clear.

After completing the above settings, click Run Step or Run Project to view the recognition results in the data visualization area. In the Result Details, you can view the Number of Recognition Results and the Running Cycle.

After confirming the recognition results, click Next to proceed to the general settings workflow.

Here, you can select the output ports based on actual requirements. By default, the 1D/2D barcode content is output.

After checking the port, the 2D Target Object Recognition Step will add the corresponding output port in real time.