Tutorialmodel cascadingpaddleocredge impulsearduino uno q
Arduino UNO Q Implements Model Cascading OCR
5.9
An author ports an Edge Impulse two-stage OCR pipeline to the Arduino UNO Q (Qualcomm Dragonwing QRB2210), deploying PaddleOCR detector and recognizer ONNX models via Edge Impulse BYOM. The workflow runs a lightweight detector at high FPS and conditionally invokes a heavier recognizer on cropped bounding boxes to reduce FLOPs. The proof-of-concept is runnable via a Python web UI but remains slow on the UNO Q.
Key Points
- 1Implements model cascading using lightweight detector and heavy recognizer to perform OCR on Arduino UNO Q
- 2Reduces FLOPs by cropping to detector bounding boxes, cutting recognition workload and improving throughput
- 3Enables efficient edge OCR prototypes; practitioners can deploy PaddleOCR ONNX via Edge Impulse BYOM
Scoring Rationale
Practical deployment guide with runnable steps, but niche hardware focus and limited novelty reduce broader industry impact.
Sources
Public references used for this report.
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