Add unified CRNN whole-plate reader (segmentation-free) + update 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 ALPR Models — Iranian License Plates (ONNX)
+
+A complete, portable model bundle for reading **Iranian vehicle license plates**,
+powering the **Platrix** real‑time ALPR engine. Every model ships as a portable
+**ONNX** graph, so it runs anywhere [ONNX Runtime](https://onnxruntime.ai/) runs —
+no TensorFlow or PyTorch required at inference time.
+
+The recommended pipeline is two models: **detect the plate** (YOLO) → **read the
+whole plate** (CRNN).
+
+- **Author:** [Dibachain](https://huggingface.co/Dibachain)
+
+> **Project & source code:** **https://github.com/AliAkrami1375/Platrix**
+
+---
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `plate_yolo.onnx` | **Plate detector** — a YOLOv8 model that localizes the plate in a full frame (single "plate" class). |
+| `ocr_crnn.onnx` | **Whole‑plate reader** *(recommended)* — a CRNN+CTC model that reads the entire plate in one pass (no character segmentation). Input `1×1×32×128` grayscale; output `(T, 33)` logits, CTC‑decoded. Trained on realistic full‑plate images with the official plate font + augmentation. |
+| `ocr_crnn.labels.json` | Class list for the CRNN (CTC blank is the last index). |
+| `ocr_cnn.onnx` | **Per‑character classifier** *(alternative)* — a small CNN that classifies a single segmented glyph (`1×1×32×32` → 28 classes). Trained on real plate character crops. |
+| `ocr_cnn.labels.json` | Class list for the per‑character CNN. |
+
+---
+
+## How the pipeline works
+
+1. **Detect** the plate in the frame with `plate_yolo.onnx`.
+2. **Read** the whole plate crop with `ocr_crnn.onnx` (CRNN + CTC) — no character
+   segmentation, so there are no split/merge errors. Output is decoded with a
+   greedy CTC pass into the plate string.
+
+The CRNN is trained on realistic full‑plate images rendered with the **official
+Iranian plate font and layout**, plus perspective / rotation / motion‑blur /
+illumination augmentation, so it transfers to real plate crops.
+
+---
+
+## Usage — whole‑plate reader (recommended)
+
+```python
+import json
+import numpy as np
+import cv2
+import onnxruntime as ort
+from huggingface_hub import hf_hub_download
+
+onnx = hf_hub_download("Dibachain/ocr-persian", "ocr_crnn.onnx")
+labels = json.load(open(hf_hub_download("Dibachain/ocr-persian", "ocr_crnn.labels.json"), encoding="utf-8"))
+blank = len(labels)  # CTC blank is the last index
+
+sess = ort.InferenceSession(onnx, providers=["CPUExecutionProvider"])
+name = sess.get_inputs()[0].name
+
+def read_plate(plate_bgr):
+    gray = cv2.cvtColor(plate_bgr, cv2.COLOR_BGR2GRAY)
+    img = cv2.resize(gray, (128, 32)).astype("float32") / 255.0
+    logits = sess.run(None, {name: img.reshape(1, 1, 32, 128)})[0][0]  # (T, C)
+    idx = logits.argmax(1)
+    out, prev = [], -1
+    for i in idx:
+        if i != blank and i != prev and i < len(labels):
+            out.append(labels[int(i)])
+        prev = int(i)
+    return "".join(out)      # e.g. "81و63813"
+```
+
+### With Platrix (end‑to‑end)
+
+```bash
+git clone https://github.com/AliAkrami1375/Platrix.git
+cd Platrix && pip install -r requirements.txt
+huggingface-cli download Dibachain/ocr-persian \
+    ocr_crnn.onnx ocr_crnn.labels.json plate_yolo.onnx --local-dir models/
+platrix serve      # auto-uses YOLO + CRNN, dashboard on http://localhost:8080
+```
+
+---
+
+## Labels
+
+- **CRNN** (`ocr_crnn.labels.json`): the digits `0–9` and Persian plate letters
+  (`ا ب پ ت ث ج ح د ز ژ س ص ط ع ق ل م ن و ه ی`). The CTC **blank** is the extra,
+  last index (`len(labels)`).
+- **Per‑character CNN** (`ocr_cnn.labels.json`): 28 classes, one neuron per glyph.
+
+---
+
+## Model architectures
+
+- **CRNN reader:** a 5‑block CNN → 2‑layer bidirectional LSTM → linear, trained
+  with CTC loss. Input `1×1×32×128`. ~96% exact‑plate accuracy on held‑out
+  synthetic plates and strong transfer to real photos.
+- **Per‑character CNN:** `Conv → Conv → Pool → Conv → Pool → FC → FC`, trained on
+  real plate character crops (~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

ocr_crnn.labels.json

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

ocr_crnn.onnx

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+oid sha256:9ff09e9d14585c4227647344b52eb4cc2d3292e5b93ee1c5f68d5ece59943a18
+size 10452525

plate_yolo.onnx

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+size 12608775