| --- |
| license: apache-2.0 |
| tags: |
| - ocr |
| - paddleocr |
| - torq |
| - synaptics |
| - npu |
| library_name: torq |
| --- |
| |
| # PP-OCRv6-tiny for Torq (SL2619 NPU) |
|
|
| PP-OCRv6-tiny optical character recognition compiled for the Synaptics Torq NPU: |
| DBNet text detection followed by CTC text recognition. Both stages run on the |
| NPU. Used by the `ppocr` demo in |
| [torq-examples](https://github.com/synaptics-torq/torq-examples). |
|
|
| The recognition dictionary is Chinese + English (6,904 characters), so Latin |
| text, digits and punctuation decode natively; Japanese, Korean, Cyrillic and |
| Arabic are not covered. |
|
|
| | Input — `samples/sample.jpg` | NPU output — boxes + recognized text | |
| |:---:|:---:| |
| | <img src="https://huggingface.co/Synaptics/paddle-paddle-tiny/resolve/main/samples/sample.jpg" width="380"> | <img src="https://huggingface.co/Synaptics/paddle-paddle-tiny/resolve/main/assets/sample_ocr.jpg" width="380"> | |
|
|
| All ten lines are read correctly at confidence ≥ 0.966, in 1.7 s end to end on |
| an SL2619. |
|
|
| ## Files |
|
|
| | File | Purpose | |
| |---|---| |
| | `ppocr_det_800x608.vmfb` | Detection (DBNet), static 800×608 bf16 input | |
| | `rec_buckets/rec_w320.vmfb` | Recognition, 48×320 lines | |
| | `rec_buckets/rec_w640.vmfb` | Recognition, 48×640 lines | |
| | `rec_buckets/rec_w1280.vmfb` | Recognition, 48×1280 lines | |
| | `rec_buckets/rec_w2432.vmfb` | Recognition, 48×2432 lines | |
| | `ppocr_rec.yml` | Recognizer character dictionary | |
| | `ppocr_det_dynamic.onnx` | fp32 detection, CPU reference for accuracy checks | |
| | `ppocr_rec_dynamic.onnx` | fp32 recognition, CPU reference for accuracy checks | |
| | `samples/sample.jpg` | Sample café menu card, 10 text lines | |
|
|
| ### Why four recognition models |
|
|
| Recognition input width is static per vmfb. Each detected line is routed to the |
| narrowest bucket it fits in, so a short label is padded to 320 rather than to |
| the widest width. Lines longer than 2432 clamp to the widest bucket. |
|
|
| ## Usage |
|
|
| ```sh |
| git clone https://github.com/synaptics-torq/torq-examples |
| cd torq-examples |
| python setup_demos.py ppocr |
| |
| cd ppocr |
| python src/infer.py \ |
| --image ../models/Synaptics/paddle-paddle-tiny/samples/sample.jpg \ |
| --models ../models/Synaptics/paddle-paddle-tiny \ |
| --save-image |
| ``` |
|
|
| Either stage can be switched to ONNX Runtime with `--det-backend ort` / |
| `--rec-backend ort` (plus the matching `--det-onnx` / `--rec-onnx`) to compare |
| NPU output against a CPU reference. |
|
|
| ## Measured on SL2619 |
|
|
| `samples/sample.jpg`, 912×1200, 10 text lines detected: |
|
|
| | Stage | Time | |
| |---|---| |
| | Detection (800×608) | ~0.53 s | |
| | Recognition (10 lines, bucketed) | ~1.19 s | |
|
|
| Recognition scales with the number of detected lines, because each line is a |
| separate invocation — the bucket models are compiled with a static batch of 1. |
| A dense page of 99 lines takes roughly 22 s. |
|
|
| ### About `sample.jpg` |
|
|
| A 912×1200 café menu card, rendered synthetically in DejaVu Serif rather than |
| photographed, so it carries no third-party image licensing. |
|
|
| Its width/height ratio of 0.76 matches the detector's static 608×800 input. |
| Preprocessing resizes straight to that shape without preserving aspect, so an |
| off-ratio image reaches the model stretched — worth matching if you swap in your |
| own sample. |
|
|
| Recognized output, all ten lines at confidence ≥ 0.966: |
|
|
| ``` |
| 1 [0.991] BLUE DOOR CAFE 6 [0.995] Smoked Salmon Bagel 9.75 |
| 2 [0.996] all day breakfast 7 [1.000] DRINKS |
| 3 [0.999] BREAKFAST 8 [0.975] Espresso 2.75 |
| 4 [0.996] Avocado Toast 6.50 9 [0.966] Fresh Orange Juice 4.00 |
| 5 [0.996] Buttermilk Pancakes 7.00 10 [0.993] open 7am - 3pm daily |
| ``` |
|
|
