paddle-paddle-tiny

Synaptics/paddle-paddle-tiny

paddle-paddle-tiny is a machine learning model from Synaptics. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for torq. The card lists the license as apache-2.0.

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.

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Updated Sep 3, 2026

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At a glance

Library: torq
License: apache-2.0
Access: Public
Created: Aug 13, 2026
Updated: Sep 3, 2026
SHA: 3f006a3e

Model card

From the Hugging Face model README

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
<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
`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

Library: torq
License: apache-2.0
Created: Aug 13, 2026
Updated: Sep 3, 2026