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Lpr Ocr Costa Rica

98.4% plate accuracy · 4.4MB ONNX · CTC architecture · Real-time inference

TypeOtherVersionRevision e9ba489CClasse

Source du modèle

Type
Other
Taille du fichier
4,38 MB
Version
vRevision e9ba489
Mis à jour
14 sept. 2026
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0
Source
Hugging Face

Extrait de la source

98.4% plate accuracy · 4.4MB ONNX · CTC architecture · Real-time inference

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1 source
Hugging FacePrincipalVersion e9ba489f83682b57b80d67e5835217adc34871d4
huggingface.co/xmichael/lpr-ocr-costa-rica/tree/e9ba489f83682b57b80d67e5835217adc34871d4

Vérifié 20 août

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wink-lpr-ocr-cr.onnx

onnx · 4,38 MB · SHA-256 3618b71ad906…616d · Hugging Face

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--- license: cc-by-4.0 language: - es tags: - license-plate-recognition - ocr - alpr - costa-rica - latin-america - onnx - ctc - crnn library_name: onnxruntime pipeline_tag: image-to-text datasets: [] metrics: - accuracy model-index: - name: wink-lpr-ocr-cr results: - task: type: image-to-text name: License Plate OCR metrics: - type: accuracy value: 98.4 name: Plate Accuracy - type: accuracy value: 97.8 name: Character Accuracy --- <div align="center"> <img src="https://www.wink.co/img/winklogo.png" width="200" alt="WINK Streaming"> # WINK LPR — Costa Rica Plate OCR **98.4% plate accuracy** · 4.4MB ONNX · CTC architecture · Real-time inference [![WINK Streaming](https://img.shields.io/badge/WINK-Streaming-blue?style=flat-square)](https://www.wink.co) [![License](https://img.shields.io/badge/License-CC--BY--4.0-green?style=flat-square)](https://creativecommons.org/licenses/by/4.0/) [![ONNX](https://img.shields.io/badge/Format-ONNX-orange?style=flat-square)](https://onnx.ai/) </div> ## Overview A high-accuracy OCR model for reading Costa Rica license plates, developed by [WINK Streaming](https://www.wink.co) as part of a production LPR (License Plate Recognition) system. The model reads cropped plate images and outputs the plate text. It handles both daytime color and nighttime IR/grayscale camera feeds. ## Model Details | Property | Value | |----------|-------| | **Architecture** | CNN (5 conv blocks) + BiLSTM (128 hidden × 2 layers) + CTC decoder | | **Parameters** | 1,117,170 | | **Input** | `[batch, 96, 192, 1]` — grayscale, NHWC, uint8 (0-255) | | **Output** | `[batch, 48, 38]` — 48 CTC timesteps, 38 classes | | **Alphabet** | `0-9`, `A-Z`, `_` (pad) + CTC blank (index 37) | | **ONNX size** | 4.4 MB | | **ONNX opset** | 18 (IR version 8) | | **Inference** | ~5ms CPU, ~2ms GPU | ## Performance | Metric | Score | |--------|-------| | **Plate accuracy** (exact match) | **98.4%** | | **Character accuracy** | **97.8%** | | Validation set | 9,074 crops | | Training set | 81,666 crops from 9,727 labeled detections | Evaluated on held-out 10% split of production data from Axis & Hikvision cameras (2304×1296, color day / IR night). ## Supported Plate Formats - **Standard:** 3 letters + 3 digits (e.g. `AAP096`) - **Numeric:** All digits (e.g. `672625`) - **Government:** 2-3 letters + 2 digits - **Motorcycle:** All digits, 2-r...

Source context: 0 downloads · 0 likes · Pipeline image-to-text · Library onnxruntime · Repo xmichael/lpr-ocr-costa-rica