What are the input and output tensors for detection and recognition from a .onnx file?

[thebest132]

What do I forward and what is the result and how do I interpret it?

[GreatV]

The input and output tensors for detection and recognition from a .onnx file depend on whether you are using the detection model or the recognition model.

1. Input Tensor:

For both detection and recognition models, the input tensor is an image represented as a 4D tensor with the format:

(batch_size, num_channels, height, width)

• batch_size: The number of images processed at once. This is typically 1 for single-image inference.
• num_channels: The number of channels in the image, usually 3 for RGB images.
• height, width: The dimensions of the input image.

For example, an image input tensor might have a shape like:

(1, 3, H, W)  # Where H and W are dynamically defined image dimensions.

2. Output Tensors:

Detection Model Output:

The detection model outputs bounding boxes around detected text regions. The output typically consists of:

• dt_boxes: A list of detected bounding boxes in the format:

  [[x1, y1, x2, y2, x3, y3, x4, y4], ...]
  

  Each bounding box contains the coordinates of the four corners of the detected text region.

Recognition Model Output:

The recognition model takes each cropped text image from the detection step and produces the recognized text along with confidence scores.

• rec_res: A list of recognized text strings with their confidence scores:

  [("text_1", confidence_1), ("text_2", confidence_2), ...]
  

  For example:

  [("Hello", 0.99), ("World", 0.98)]
  

3. How to Forward the Input and Interpret the Output

1. Load the ONNX model using ONNXRuntime:

   import onnxruntime as ort
   import numpy as np
   
   session = ort.InferenceSession("model.onnx")
   input_name = session.get_inputs()[0].name

2. Preprocess the input image:

   • Convert the image to a NumPy array.
   • Normalize pixel values (if required).
   • Resize to the expected input size.
   • Convert to float32 and reshape to (1, 3, H, W).

   image_input = np.random.rand(1, 3, H, W).astype(np.float32)

3. Run inference:

   outputs = session.run(None, {input_name: image_input})

4. Interpret the output:

   • For detection, extract bounding box coordinates.
   • For recognition, map output probabilities to text characters using a character dictionary.

By following this workflow, you can use the .onnx models for text detection and recognition in OCR tasks.

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