Platrix / README.md

Update to real-plate-trained model (Iranis, 28 classes, ~98% val)

e39229d verified 8 days ago

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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 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

Project & source code: https://github.com/AliAkrami1375/Platrix

Files

File	Description
`ocr_cnn.onnx`	The inference graph (opset 13)
`ocr_cnn.labels.json`	Ordered list mapping each output neuron to its character

How the pipeline works

Platrix reads a plate in three stages; this model is stage 3:

Detect the plate region in the frame.
Segment the plate into individual character images (normalized to a white‑on‑black 32 × 32 glyph).
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

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)

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.

Dibachain
/

Platrix