A high‑performance License Plate Recognition (LPR) engine specifically designed for Guatemala license plates, implemented entirely in native C++.
The engine achieves >99% recognition accuracy, delivers very high throughput, and is optimized for deployment across a wide range of devices — from embedded systems to high‑performance servers. It is built without any third‑party libraries, ensuring full control, portability, and minimal runtime overhead.
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Country‑Specific Optimization
Tailored exclusively for Guatemala license plate formats, fonts, and layout characteristics. -
Exceptional Accuracy
Achieves over 99% recognition accuracy on validated datasets. -
Ultra‑Fast Recognition
Designed for real‑time and high‑throughput scenarios such as traffic monitoring and access control. -
Pure C++ Implementation
Written entirely in standard C++ with zero third‑party dependencies. -
Lightweight & Portable
Can be deployed on:- Embedded devices
- Edge devices
- Desktop applications
- Server environments
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Neural Network Classifier
Uses an MQDF (Modified Quadratic Discriminant Function) neural network classifier for robust and efficient character recognition.
- C++ (Native)
- Custom‑built image preprocessing pipeline
- Plate localization optimized for Guatemala plates
- Character segmentation and normalization
- MQDF Neural Network Classifier for character recognition
- None
The engine does not rely on OpenCV, TensorFlow, ONNX, or any other third‑party libraries.
| Metric | Description |
|---|---|
| Accuracy | >99% on Guatemala plates |
| Recognition Speed | Real‑time, high‑FPS capable |
| Memory Footprint | Very low |
| Startup Time | Instant (no model loading frameworks) |
The engine is optimized for low latency and high concurrency, making it suitable for continuous video streams and large‑scale deployments.
- Standard Guatemala private vehicle plates
- Commercial vehicle plates
- Government‑issued formats (where applicable)
Note: The engine is optimized for Guatemala plates only. Recognition accuracy for other countries is not guaranteed.
- Traffic monitoring systems
- Parking management systems
- Toll collection systems
- Access control and security gates
- Smart city and IoT solutions
- Embedded and edge AI devices
- C++11 or later compatible compiler
- Windows / Linux (portable codebase)
The engine is designed to be:
- Embedded directly into existing C++ applications
- Wrapped for use with other languages (e.g., C#, Java, Python) via FFI or bindings
- Integrated into real‑time video processing pipelines
- Accuracy First – Optimized neural classifier and preprocessing
- Performance First – No unnecessary abstractions or dependencies
- Portability – Runs anywhere a C++ compiler is available
- Reliability – Deterministic execution and predictable resource usage
- Country‑specific: optimized only for Guatemala license plates
- Does not include UI or camera capture modules
- Image acquisition and video decoding must be handled externally
- Additional Guatemala plate variants
- Multi‑threaded batch recognition APIs
- Optional GPU acceleration
- SDK packaging for commercial deployment
Developed by [Your Name / Organization]
Senior C++ / Computer Vision Engineer
This project is proprietary. All rights reserved.
Unauthorized copying, distribution, or use without explicit permission is prohibited.
For licensing, commercial integration, or technical inquiries:
- Email: [jchueng0922@outlook.com]
- Telegram: https://t.me/jcheung0922
- WhatsApp: +852 4639 4008