---
license: apache-2.0
tags:
- executorch
- xnnpack
- pte
- on-device
- image-to-text
- text-recognition
---
# docTR CRNN-MobileNetV3-Small — ExecuTorch

Text **recognition**: reads one word crop. The other half of the pair is
[DB-MobileNetV3-Large](https://huggingface.co/mlboydaisuke/docTR-DB-MobileNetV3-Large-ExecuTorch),
which finds the crops.

- **Source**: [mindee/doctr](https://github.com/mindee/doctr) `crnn_mobilenet_v3_small`,
  2.1M parameters
- **License**: Apache-2.0
- **Input**: `[1, 3, 32, 128]`, normalised with docTR's own mean (0.694, 0.695, 0.693) and
  std (0.299, 0.296, 0.301)
- **Output**: `[1, 32, 127]` — 32 timesteps over a 126-symbol alphabet plus the CTC blank

Logits, not probabilities: greedy CTC only needs the argmax per timestep, so a softmax would
be thrown away. The alphabet is
`0123456789`, `a-z`, `A-Z`, punctuation, `°£€¥¢฿`, and accented Latin — read it out of
`crnn_mobilenet_v3_small().cfg["vocab"]` rather than retyping it.

## Verification (Mac arm64, 2026-08-23)

| build | size | latency | worst corr |
|---|---|---|---|
| XNNPACK fp32 | 8.8 MB | 3.2 ms | 1.000000 |
| XNNPACK fp16 | 6.5 MB | 4.0 ms | 0.997143 |
| Core ML fp32 | 6.2 MB | **0.3 ms** | 0.999530 |

Eager fp32 for the same input is 121 ms.

**Read end to end** with the detector, on a London street photograph
(`convert/check_doctr.py`):

```
boxes: 10
read: TIUZABXPRESS | Chrisiophers | Place | STREE! | LAG | BAR!
```

The signs say PIZZA EXPRESS and Christopher's Place.

## Cropping it

docTR's own `Resize` for this model is `preserve_aspect_ratio=True, symmetric_pad=False`:
the crop keeps its shape, sits at the top-left, and the rest of the strip is black. The
check does the same.

That is the principled choice rather than a measured improvement. Over four street
photographs neither it nor a plain squeeze won: one picture reads `Chrisiophers` squeezed
and `Clridabers` padded, another reads `TRAFFIC` padded and `TRAEFIC` squeezed.

## Not shipped, and why

- **int8**: worst corr **0.883**, against a 0.95 gate, for 6.1 MB against 8.8. Recognition
  is the half where a wrong character is the whole answer, so this one is not close enough.

## Conversion

```bash
python convert/export_doctr.py recognise
```

docTR's `forward` runs CTC decoding in numpy whether or not it was asked to, which
`torch.export` refuses. The model carries an `exportable` flag that skips it and returns raw
logits; the wrapper sets that.

**Feed it contiguous tensors.** ExecuTorch reads a tensor in memory order rather than by its
strides, and a crop built with `np.transpose` is not contiguous. The detector half of this
pair scored 1.000000 against eager on random input and then returned a map that thresholded
to zero boxes on a real photograph, entirely because of that.

(conversion scripts: [executorch-models](https://github.com/john-rocky/executorch-models))