Previously, I built an inference app using Hugging Face Spaces and a YOLOv5 model (trained on the NDL-DocL dataset):

Building an Inference App Using Hugging Face Spaces and a YOLOv5 Model (Trained on the NDL-DocL Dataset)
Building an Inference App Using Hugging Face Spaces and a YOLOv5 Model (Trained on the NDL-DocL Dataset)
ldas.jp

This time, I modified the app above to add JSON output, as shown in the following diff:

feat: add json output · nakamura196/yolov5-ndl-layout at 4d48b95
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huggingface.co

This enables processing using the returned results, as shown in the following notebook:

https://github.com/nakamura196/ndl_ocr/blob/main/GradioのAPIを用いた物体検出例.ipynb

There may be better approaches, but I hope this serves as a useful reference.