You will get an ANPR licence plate recognition pipeline | Computer Vision
Project details
I will build an ANPR (Automatic Number Plate Recognition) system that detects vehicle licence plates and reads the text, tuned for your country's plate format and your camera conditions.
WHAT YOU GET
▸ Licence plate detection using a YOLO model
▸ Plate text extraction via OCR, tuned to your regional format
▸ Confidence scores so you can filter uncertain reads
▸ CSV logging with timestamp, plate text and image reference (Standard and Advanced)
▸ Live video processing from RTSP camera streams (Standard and Advanced)
▸ Database storage, search API and webhook triggers (Advanced)
TYPICAL USES
Parking access control and barrier automation · gated community and site entry · vehicle logging for logistics yards · car park occupancy and duration billing · security monitoring and watchlist alerts · toll and checkpoint recording.
WHAT AFFECTS ACCURACY
Plate size in frame is the single biggest factor - the plate needs enough pixels to read. Sharp angles, motion blur at speed, dirt, glare and night conditions all reduce accuracy. Cameras positioned for plate capture typically achieve 92–98%; general-purpose CCTV pointed at a wide scene performs considerably worse.
WHAT YOU GET
▸ Licence plate detection using a YOLO model
▸ Plate text extraction via OCR, tuned to your regional format
▸ Confidence scores so you can filter uncertain reads
▸ CSV logging with timestamp, plate text and image reference (Standard and Advanced)
▸ Live video processing from RTSP camera streams (Standard and Advanced)
▸ Database storage, search API and webhook triggers (Advanced)
TYPICAL USES
Parking access control and barrier automation · gated community and site entry · vehicle logging for logistics yards · car park occupancy and duration billing · security monitoring and watchlist alerts · toll and checkpoint recording.
WHAT AFFECTS ACCURACY
Plate size in frame is the single biggest factor - the plate needs enough pixels to read. Sharp angles, motion blur at speed, dirt, glare and night conditions all reduce accuracy. Cameras positioned for plate capture typically achieve 92–98%; general-purpose CCTV pointed at a wide scene performs considerably worse.
Machine Learning Tools
deeplearn.js, fastText, NumPy, NVIDIA AI Platform, OpenCV, Python, Python Scikit-Learn, PyTorch, scikit-learn, TensorFlow, Tesseract OCRWhat's included
| Service Tiers |
Starter
$199
|
Standard
$449
|
Advanced
$899
|
|---|---|---|---|
| Delivery Time | 4 days | 8 days | 15 days |
Number of Revisions | 1 | 3 | 5 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 3 | 5 |
Number of Graphs/Charts | 2 | 5 | 6 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code |
Frequently asked questions
About Haresh
Computer Vision Engineer | YOLO, OpenCV, PyTorch & Jetson Edge AI
Surat, India - 10:18 am local time
My focus is real-time video analytics deployed on edge devices: object detection and tracking
running on NVIDIA Jetson, wired into live dashboards and alerting so the output is actually
usable by a business.
WHAT I BUILD
▸ Object Detection & Tracking - YOLO (v8/v11), custom-trained models on your own dataset, multi-object tracking, zone-based counting and dwell-time measurement.
▸ Retail & Hospitality Video Analytics - footfall counting, occupancy tracking, queue monitoring, and customer flow analysis from existing CCTV feeds.
▸ Compliance & Safety Monitoring - hygiene compliance detection, PPE detection, restricted- zone alerts, with real-time notification via Telegram, webhooks or email.
▸ ANPR / Licence Plate Recognition - plate detection, OCR, and logging pipelines for access control, parking and vehicle tracking.
▸ Edge AI Deployment - NVIDIA Jetson Orin Nano, model optimisation, TensorRT conversion, camera zone configuration, and multi-camera deployment.
▸ Dataset & Model Work - image annotation, dataset preparation, training, fine-tuning and accuracy evaluation.
WHAT MAKES MY DELIVERY DIFFERENT
Most computer vision freelancers hand you a model and a script. I deliver the whole system: the detection pipeline, the API around it, the database it writes to, and the dashboard your team actually looks at. I have a full-stack engineering background, so you don't need to hire a second developer to make the CV output usable.
TECH STACK
Vision & ML: Python · OpenCV · YOLOv8/v11 · PyTorch · TensorFlow · Roboflow · CVAT
Edge & Deployment: NVIDIA Jetson Orin Nano · TensorRT · ONNX · Docker
Delivery layer: FastAPI · REST APIs · PostgreSQL · React dashboards · WebSocket streams
Alerting: Telegram Bot API · webhooks · email/SMS triggers
HOW I WORK
Tell me what cameras you have, what environment they're in, and what you need detected. I'll tell you honestly whether it's feasible, what accuracy is realistic, and how I would approach it, before you commit to anything.
Send me a sample frame or short clip, and I will come back with a concrete plan.
(for search): computer vision engineer, computer vision developer, YOLO expert, YOLOv8 object detection, YOLOv11, OpenCV developer, PyTorch developer, deep learning engineer, edge AI deployment, NVIDIA Jetson developer, Jetson Orin Nano, TensorRT optimisation, real-time video analytics, object detection and tracking, people counting system, footfall analytics, occupancy monitoring, queue monitoring, retail video analytics, CCTV AI monitoring, surveillance AI, hygiene compliance detection, PPE detection, ANPR, licence plate recognition, image annotation specialist, dataset preparation, model training and fine-tuning, image segmentation, image classification, OCR, custom dataset creation, computer vision API, FastAPI computer vision, video processing pipeline
Steps for completing your project
After purchasing the project, send requirements so Haresh can start the project.
Delivery time starts when Haresh receives requirements from you.
Haresh works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement gathering