Synaptics/paddle-paddle-tiny

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.

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

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
ppocr_det_640x352.vmfb	Detection (DBNet), static 640×352 bf16 input, for wide/16:9 sources
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

Two detector sizes

DBNet's stride-32 backbone requires input dims that are multiples of 32. 800×608 is the default, matched to portrait documents; 640×352 serves wide/16:9 sources — it is 640×360 rounded up to the next multiple of 32, so letterboxed 16:9 content is padded rather than cropped. Select it in the demo with --det-hw 640 352.

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

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