Add YOLO plate detector + end-to-end model card

README.md

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+---
+license: mit
+library_name: onnx
+pipeline_tag: image-classification
+tags:
+  - ocr
+  - persian
+  - farsi
+  - alpr
+  - anpr
+  - license-plate
+  - iran
+  - onnx
+  - computer-vision
+language:
+  - fa
+---
+
+# Persian OCR — Character Recognition (ONNX)
+
+A compact convolutional neural network that recognizes **Persian license‑plate
+characters** — the digits `0–9` and the Persian letters used on Iranian vehicle
+plates — trained on **real‑world plate character crops**. It is the OCR stage of
+the **Platrix** real‑time ALPR engine and ships as a portable **ONNX** graph, so
+it runs anywhere [ONNX Runtime](https://onnxruntime.ai/) runs — no TensorFlow or
+PyTorch required at inference time.
+
+- **Task:** single‑character image classification (28 classes)
+- **Input:** `1 × 1 × 32 × 32` grayscale tensor, values in `[0, 1]` (white glyph on black)
+- **Output:** `1 × 44` logits → `argmax` → character via the bundled label map
+- **Format:** ONNX (opset 13), ~2.2 MB
+- **Author:** [Dibachain](https://huggingface.co/Dibachain)
+
+> **Project & source code:** **https://github.com/AliAkrami1375/Platrix**
+
+---
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `ocr_cnn.onnx` | Character-recognition CNN (opset 13) |
+| `ocr_cnn.labels.json` | Ordered list mapping each output neuron to its character |
+| `plate_yolo.onnx` | **Plate detector** — a YOLOv8n model that localizes plates in a full frame (single "plate" class). Pair it with the OCR for end-to-end reading. |
+
+---
+
+## How the pipeline works
+
+Platrix reads a plate in three stages; this model is stage 3:
+
+1. **Detect** the plate region in the frame.
+2. **Segment** the plate into individual character images (normalized to a
+   white‑on‑black `32 × 32` glyph).
+3. **Recognize** each glyph with this model and assemble the plate string.
+
+The training images are preprocessed **identically** to the segmenter's output
+(Otsu binarization, tight crop, square‑pad, resize) so the model sees the same
+glyph framing in training and in production. Training also applies random affine
+augmentation (scale / shift / rotation) for robustness to how characters are
+framed.
+
+---
+
+## Usage
+
+```python
+import json
+import numpy as np
+import onnxruntime as ort
+from huggingface_hub import hf_hub_download
+
+onnx_path = hf_hub_download("Dibachain/ocr-persian", "ocr_cnn.onnx")
+labels_path = hf_hub_download("Dibachain/ocr-persian", "ocr_cnn.labels.json")
+
+labels = json.load(open(labels_path, encoding="utf-8"))
+session = ort.InferenceSession(onnx_path, providers=["CPUExecutionProvider"])
+input_name = session.get_inputs()[0].name
+
+def classify(glyph_32x32_uint8):
+    """glyph: a 32x32 grayscale image, white character on black background."""
+    x = (glyph_32x32_uint8.astype("float32") / 255.0).reshape(1, 1, 32, 32)
+    logits = session.run(None, {input_name: x})[0]
+    return labels[int(np.argmax(logits))]
+```
+
+### With Platrix (end‑to‑end plate reading)
+
+```bash
+git clone https://github.com/AliAkrami1375/Platrix.git
+cd Platrix
+pip install -r requirements.txt
+# place ocr_cnn.onnx + ocr_cnn.labels.json in ./models/
+PLATRIX_OCR=onnx platrix serve      # dashboard on http://localhost:8080
+```
+
+---
+
+## Labels
+
+28 classes: the digits `0–9` and the Persian letters that appear on Iranian
+plates (`ا ب پ ت ج د س ص ط ع ق ل م ن و ه ی` and special markers). Each neuron
+maps to exactly one plate character; the exact order is in `ocr_cnn.labels.json`.
+
+---
+
+## Model architecture
+
+A small CNN: `Conv(32) → Conv(32) → MaxPool → Conv(64) → MaxPool → FC(128) → FC(28)`
+with dropout, trained with Adam and cross‑entropy on real plate character crops
+with affine augmentation (~98% validation accuracy).
+
+---
+
+## Intended use & limitations
+
+- **Intended for** parking, access control and traffic‑analytics pipelines that
+  first detect and segment plates, then classify each character with this model.
+- **Not** an end‑to‑end plate reader on its own — it classifies **single,
+  pre‑segmented characters**. Overall accuracy on a full plate depends on the
+  quality of the upstream detection and segmentation.
+- Real‑world plates vary in font, angle, lighting and wear; for best results,
+  pair with a strong plate detector and clean segmentation.
+
+---
+
+## License
+
+Released under the **MIT License**. © Dibachain.

ocr_cnn.labels.json

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+[
+  "0",
+  "1",
+  "2",
+  "3",
+  "4",
+  "5",
+  "6",
+  "7",
+  "8",
+  "9",
+  "ا",
+  "ب",
+  "ت",
+  "ج",
+  "د",
+  "س",
+  "ص",
+  "ط",
+  "ع",
+  "ق",
+  "ل",
+  "م",
+  "ن",
+  "ه",
+  "و",
+  "پ",
+  "ژ",
+  "ی"
+]

ocr_cnn.onnx

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+oid sha256:7d573c51cc855a8e080f1f88597477f4fb5a2b9cafa1bb125bd6038e441f5bca
+size 2226402

plate_yolo.onnx

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+oid sha256:a54e475c402e6036bb5c70f1a6ff75179e76098a5c8039bb5d148c0b6421f5c6
+size 12608775