| --- |
| 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. |
|
|
