How AI, Computer Vision & OCR Transform Vehicle Identification — from toll booths to smart cities and beyond.
Automatic Number Plate Recognition (ANPR) combines high-resolution cameras, optical character recognition (OCR), and machine learning to instantly read and process vehicle licence plates — with no human intervention required.
Used globally across industries, ANPR enables seamless automation of processes that once required manual checks: cashless toll collection, smart parking, warehouse loading bays, vehicle entry & exit logging, and community safety management.
From camera capture to gate action — a 5-step AI pipeline that completes in under one second.
High-res IR camera captures the vehicle at trigger point or via continuous video stream.
YOLOv8 deep-learning model locates and crops the plate region from the full frame.
EasyOCR extracts characters across multiple preprocessing passes with regex-scored validation.
Plate cross-referenced against SQLite / cloud whitelist of authorised vehicles in milliseconds.
Gate opens for whitelisted plates. Unknown vehicles trigger Telegram / WhatsApp alert. All events logged.
High-res IR camera captures the vehicle at trigger point or via continuous video stream.
YOLOv8 deep-learning model locates and crops the plate region from the full frame.
EasyOCR extracts characters with regex-scored validation across multiple passes.
Plate cross-referenced against SQLite whitelist of authorised vehicles.
Gate opens for whitelisted plates. Unknown vehicles trigger Telegram / WhatsApp alert. All events logged.
ANPR technology powers critical infrastructure across every industry segment.
Cashless, barrier-free toll automation eliminates queuing and manual payment. Vehicles are billed automatically via linked accounts.
Automated entry/exit and duration-based billing. Vehicles enter and exit without stopping — the system handles everything in the background.
Instant cross-reference against stolen vehicle databases, expired registrations, and wanted persons. Real-time alerts to patrol officers.
Real-time flow monitoring, congestion analysis, and origin-destination studies. Data feeds into adaptive traffic signal systems.
Whitelist-based vehicle access for gated communities, corporate campuses, and restricted zones. Integrates with existing boom gates.
Cross-border vehicle verification against international databases. Handles multi-format plates from different countries automatically.
Eliminates manual toll and parking staff. One system handles thousands of vehicles per day, 24/7, with minimal maintenance overhead.
Deploy from a single gate to a city-wide multi-camera network. Cloud architecture scales horizontally with no redesign required.
Works with IoT sensors, cloud platforms, and existing CCTV infrastructure via MQTT and REST API output streams out of the box.
ESP32-S3 driven 192×32 outdoor P5 panel shows the detected plate in real-time via MQTT — instant visual confirmation at the gate.
Choose the deployment model that best fits your infrastructure and operational requirements.
Tailored to Malaysian plate formats, JPJ database standards, and the unique traffic patterns of our roads.
Kuala Lumpur — Standard
Sabah — Short format
Johor — State prefix
Single-letter prefix
Regex:
[A-Z]{'{1,3}'}\\d{'{1,4}'}[A-Z]{'{0,2}'}
Handles standard, short, and all state-prefix formats
Multi-lane free-flow toll collection with automatic account deduction. Plate acts as the vehicle identifier, removing the need for RFID tags.
Cross-reference plates against Jabatan Pengangkutan Jalan (JPJ) records for roadworthiness, ownership, and registration status verification.
Security guards and condo management receive instant notifications with plate photo when an unknown vehicle enters the premises.
Replace manual logbooks with automated ANPR — residents pass through automatically; visitors trigger guard notification via Telegram.
Replace with real Malaysian toll / condominium gate photo
The pipeline begins with YOLOv8 nano detecting vehicle bounding boxes, followed by OpenCV preprocessing (grayscale, histogram equalisation, morphological operations) applied in multiple passes to the cropped plate region. EasyOCR runs character recognition with regex-scored candidate validation. Confirmed plates are persisted to SQLite and published over MQTT to edge devices — including ESP32-S3 RGB LED matrix displays and Telegram bots.
This system was developed independently and locally by a coding teacher, driven by a passion for accessible AI and smart automation solutions for Malaysian communities.
New AI features will be continuously updated and tailored according to customer needs — ensuring the system evolves alongside the latest advancements in computer vision and plate recognition technology.
We welcome constructive feedback from users, communities, and partners to help us build a better, smarter, and more reliable ANPR experience.