AI-Based Automatic Number Plate Recognition with Crime Database Matching

AI-Based Automatic Number Plate Recognition (ANPR) with Crime Database Matching AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Dashboard

AI-Based Automatic Number Plate Recognition (ANPR) with Crime Database Matching AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Dashboard 1. Full Project Description This project is an AI-powered smart surveillance and alert system designed to automatically detect vehicle number plates using computer vision, compare them against a crime/stolen vehicle database, and instantly send alerts through Telegram voice notifications, cloud dashboards, and Google Sheets logging. The system combines: ESP32-CAM for image capture AI-based OCR/ANPR for license plate extraction n8n automation workflows Telegram bot notifications ThingSpeak IoT dashboard Google Sheets cloud logging Agentic AI logic for predictive monitoring Voice notification alerts The solution can be deployed in: Smart cities Toll plazas Police checkpoints Parking systems Campus security Border surveillance Highway monitoring 2. System Architecture ESP32-CAM ↓ WiFi Upload ↓ n8n Webhook ↓ AI OCR Processing ↓ Number Plate Extraction ↓ Crime Database Matching ↓ ┌───────────────┬────────────────┬─────────────────┐ ↓ ↓ ↓ Telegram Alert Google Sheets ThingSpeak Cloud Voice Message Data Logging Live Dashboard 3. Main Features Core Features AI-Based Number Plate Recognition OCR extracts vehicle registration number Supports multiple plate formats Crime Database Matching Compares plate with: stolen vehicle list blacklist database wanted vehicles Telegram Instant Alerts Text notification Voice notification Snapshot image Google Sheets Logging Stores: Vehicle number Date/time Match status GPS location Confidence score ThingSpeak IoT Dashboard Displays: Vehicle count Crime detections Daily trends AI analytics AI Power Consumption Prediction Predicts: Battery usage Camera activity Transmission load 4. Components List Component Quantity ESP32-CAM Module 1 FTDI Programmer 1 OV2640 Camera 1 5V Power Supply 1 Breadboard 1 Jumper Wires Several MicroSD Card 1 WiFi Router 1 USB Cable 1 Buzzer (optional) 1 Relay Module (optional) 1 GPS Module NEO-6M (optional) 1 OLED Display (optional) 1 Solar Panel + Battery (optional) 1 5. Circuit Schematic Diagram ESP32-CAM Basic Wiring FTDI ESP32-CAM -------------------------- 5V → 5V GND → GND TX → U0R RX → U0T GPIO0 → GND (Programming Mode) Optional Buzzer Buzzer + → GPIO12 Buzzer - → GND 6. Flowchart START ↓ ESP32 Captures Image ↓ Send Image to n8n Webhook ↓ AI OCR Extracts Number Plate ↓ Check Crime Database ↓ Is Match Found? ┌───────────────┐ │ YES │ ↓ │ NO Send Telegram │ Voice Alert │ ↓ │ Update Sheets │ ↓ │ Update Dashboard │ ↓ │ END │ ↓ Log Normal Vehicle ↓ END 7. ESP32 Source Code (Arduino IDE) Required Libraries Install: WiFi.h HTTPClient.h esp_camera.h ESP32 Code #include "WiFi.h" #include "HTTPClient.h" #include "esp_camera.h" const char* ssid = "YOUR_WIFI"; const char* password = "YOUR_PASSWORD"; String serverName = "https://your-n8n-instance/webhook/anpr"; void startCamera(); void setup() { Serial.begin(115200); WiFi.begin(ssid, password); while (WiFi.status() != WL_CONNECTED) { delay(500); Serial.print("."); } Serial.println("WiFi Connected"); startCamera(); } void loop() { camera_fb_t * fb = esp_camera_fb_get(); if(!fb) { Serial.println("Camera capture failed"); return; } HTTPClient http; http.begin(serverName); http.addHeader("Content-Type", "image/jpeg"); int response = http.POST(fb->buf, fb->len); Serial.println(response); http.end(); esp_camera_fb_return(fb); delay(10000); } void startCamera() { camera_config_t config; config.ledc_channel = LEDC_CHANNEL_0; config.ledc_timer = LEDC_TIMER_0; config.pin_d0 = 5; config.pin_d1 = 18; config.pin_d2 = 19; config.pin_d3 = 21; config.pin_d4 = 36; config.pin_d5 = 39; config.pin_d6 = 34; config.pin_d7 = 35; config.pin_xclk = 0; config.pin_pclk = 22; config.pin_vsync = 25; config.pin_href = 23; config.pin_sscb_sda = 26; config.pin_sscb_scl = 27; config.pin_pwdn = 32; config.pin_reset = -1; config.xclk_freq_hz = 20000000; config.pixel_format = PIXFORMAT_JPEG; esp_camera_init(&config); } 8. n8n Workflow Overview Workflow Nodes Webhook Trigger ↓ Image OCR API ↓ Extract Plate Number ↓ IF Node (Crime Match?) ┌──────────────┬──────────────┐ ↓ YES ↓ NO Telegram Alert Store Data ↓ Google Sheets ↓ ThingSpeak Update 9. Sample n8n Workflow JSON Structure { "nodes": [ { "name": "Webhook", "type": "n8n-nodes-base.webhook" }, { "name": "OCR API", "type": "n8n-nodes-base.httpRequest" }, { "name": "Check Database", "type": "n8n-nodes-base.if" }, { "name": "Telegram", "type": "n8n-nodes-base.telegram" } ] } 10. Telegram Bot Setup Step 1: Create Bot Open Telegram and search: Telegram Use: BotFather Commands: /newbot Copy: Bot Token Step 2: Get Chat ID Send a message to your bot. Open: Telegram API GetUpdates Example: https://api.telegram.org/botTOKEN/getUpdates Find: chat.id 11. Telegram Voice Notification Automation Text-to-Speech Flow Detected Plate ↓ Generate Alert Text ↓ Google TTS API ↓ MP3 Audio File ↓ Telegram Voice Message Example Alert Warning! Blacklisted vehicle detected. Vehicle Number AP09AB1234 Location: Highway Gate 2 12. Google Sheets Integration Required Setup Open: Google Sheets Columns: Time Vehicle No Match Confidence Location Use: Google Sheets node in n8n Authentication: Google OAuth2 13. ThingSpeak Cloud Dashboard Setup Create account at: ThingSpeak Create Fields Field Purpose Field 1 Vehicle Count Field 2 Crime Matches Field 3 AI Confidence Field 4 Power Usage API Example https://api.thingspeak.com/update?api_key=XXXX&field1=20 14. AI Power Consumption Prediction Logic AI Logic Inputs Camera ON time WiFi transmission frequency CPU load Night/day mode Alert frequency Prediction Formula Power Usage = (Camera Active Time × Current Draw) + (WiFi Transmission × Power Cost) Smart Optimization AI Agent: reduces image frequency during low traffic enters deep sleep mode activates high alert mode during suspicious activity 15. AI Agentic IoT Features Agent Behavior Autonomous Decisions Detect unusual activity Increase capture frequency Trigger emergency alerts Smart Learning Identify repeated suspicious vehicles Analyze peak crime hours Optimize bandwidth usage Predictive Analytics Vehicle traffic trends Crime hotspot prediction Battery health forecasting 16. Cloud Dashboard Features Dashboard Includes Live camera activity Detected vehicles Crime alerts GPS tracking AI confidence graph Battery status Daily statistics 17. Security Features Recommended Security API Security HTTPS webhook Token authentication Device Security Secure WiFi OTA firmware update Cloud Security Encrypted database Restricted dashboard access 18. Future Enhancements AI Improvements Deep learning vehicle recognition Face recognition integration Helmet detection Speed detection Hardware Expansion Solar-powered deployment Edge TPU acceleration 4G LTE connectivity Smart City Integration Police control room integration Traffic analytics Automatic barrier control 19. Deployment Guide Step-by-Step Deployment Hardware Assemble ESP32-CAM Upload firmware Connect to WiFi Cloud Configure n8n webhook Setup OCR API Connect Telegram bot Configure Google Sheets Setup ThingSpeak dashboard Testing Capture vehicle image Verify OCR accuracy Check alert system Validate database matching 20. Recommended OCR APIs API Accuracy OpenALPR High Plate Recognizer Very High Google Vision API High EasyOCR Medium Tesseract OCR Basic 21. Suggested AI Stack Technology Purpose ESP32-CAM Edge Device n8n Automation OpenCV Image Processing OCR AI Plate Recognition Telegram Bot Alerts Google Sheets Logging ThingSpeak IoT Dashboard MQTT Communication 22. Expected Output Example Vehicle Detected Plate Number: TS09AB1234 Status: BLACKLISTED Confidence: 96% Location: Checkpost 4 Alert Sent Successfully 23. Conclusion This project demonstrates a complete AI-powered smart surveillance ecosystem combining: Embedded IoT AI-based ANPR Cloud automation Agentic intelligence Real-time voice alerts Predictive analytics It is highly scalable for: smart cities law enforcement intelligent transportation systems automated security monitoring