How does this repository run the detection and recognition models when det and rec are enabled?

[thebest132]

In other words, what does the code have to do before inputting whatever into the models, and what is that whatever? E.g. when/does it/how does it use dict.txt? I need this to run the models as .onnx from Rust, copying the behavior and implementation of the current ocr.ocr function

[GreatV]

When both detection (det) and recognition (rec) are enabled in PaddleOCR, the process follows these steps:

1. Preprocessing the Input

• The input image is first loaded and preprocessed. This includes resizing and normalizing the image to match the model input requirements.
• If detection is enabled, the image is passed through the detection model first.

2. Text Detection Model (det)

• The detection model identifies text regions in the image and outputs bounding boxes around detected text.
• These bounding boxes are then processed (e.g., filtering, non-maximum suppression) to refine the detected text regions.

3. Cropping Detected Text Regions

• Using the bounding boxes from the detection step, the image is cropped into individual text segments.
• Each cropped image is then prepared for input into the recognition model.

4. Text Recognition Model (rec)

• Each cropped text region is passed through the recognition model.
• The recognition model converts the image into text predictions.

5. Using dict.txt

• The recognition model uses a dictionary file (e.g., dict.txt) to map the model's output probabilities to actual text characters.
• This helps in decoding the recognized text more accurately, ensuring the output aligns with the expected character set.

6. Post-processing and Output

• The recognized text is mapped to the corresponding bounding boxes.
• The final output consists of detected text areas along with their recognized text content.

How to Implement This in Rust Using ONNX Models

Since you want to replicate this behavior in Rust using ONNX models, you would need to:

1. Load the ONNX models for detection and recognition.
2. Preprocess the input image (resize, normalize).
3. Run the detection model to obtain bounding boxes.
4. Crop detected text regions based on bounding boxes.
5. Run the recognition model on each cropped text region.
6. Use a dictionary (dict.txt) to map output indices to actual characters.
7. Post-process the results to format the recognized text.

For additional details on defining models and ensuring you are using the correct ones, you may find this discussion useful:
How to define the models to use with PaddleOCR?

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