Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,79 @@
+---
+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))