```python
import argparse
import sys

def parse_args():
    parser = argparse.ArgumentParser(description="PPOCR Model Inference")
    parser.add_argument("--detection_model_path", type=str, required=True)
    parser.add_argument("--recognition_model_path", type=str, required=True)
    parser.add_argument("--table_recognition_model_path", type=str, required=True)
    parser.add_argument("--recognition_label_file", type=str, required=True)
    parser.add_argument("--table_recognition_dict_path", type=str, required=True)
    parser.add_argument("--recognition_batch_size", type=int, default=1)
    parser.add_argument("--test_image_path", type=str, required=True)
    parser.add_argument("--inference_device", type=str, default="cpu")
    parser.add_argument("--device_id", type=int, default=0)
    parser.add_argument("--backend", type=str, default="ONNX")
    return parser.parse_args()

def main():
    args = parse_args()

    # Assuming the existence of a generic model loading function and PPStructureV2Table class
    # These would need to be implemented or imported from a relevant library
    from model_loader import load_model
    from ppstructure_v2 import PPStructureV2Table
    from visualization import visualize_results
    import cv2

    # Set backend and device based on arguments
    if args.backend == "TensorRT":
        backend_options = {"use_dynamic_shape": True, "save_engine": True}
    else:
        backend_options = {}

    device_options = {"device": args.inference_device, "device_id": args.device_id}

    # Load models with options
    detection_model = load_model(args.detection_model_path, backend_options, device_options)
    recognition_model = load_model(args.recognition_model_path, backend_options, device_options)
    table_recognition_model = load_model(args.table_recognition_model_path, backend_options, device_options)

    # Set preprocessor and postprocessor for detection model
    # This is highly dependent on the model and framework being used
    # Assuming set_preprocessor and set_postprocessor are methods to configure the model
    detection_model.set_preprocessor(preprocessor_config={"type": "detection_pre"})
    detection_model.set_postprocessor(postprocessor_config={"type": "detection_post"})

    # Create PPStructureV2Table instance
    ppstructure = PPStructureV2Table(detection_model, recognition_model, table_recognition_model,
                                     recognition_batch_size=args.recognition_batch_size)

    # Read input image
    image = cv2.imread(args.test_image_path)

    # Predict and print results
    results = ppstructure.predict(image)
    print(results)

    # Visualize and save results
    visualized_image = visualize_results(image, results)
    cv2.imwrite("visualized_result.jpg", visualized_image)

if __name__ == "__main__":
    main()
```