Tech Arcanist

Autonomous Warehouse Project

The Autonomous Warehouse Project is a multi-module automation system combining AI + OCR + Robotics + IoT + Automation to bring intelligence and autonomy into warehouse operations. Developed as part of Flipkart Robotics Grid 6.0, the system automates tasks like product detection, freshness evaluation, label OCR, and automated counting. It integrates AI (Computer Vision + Deep Learning), IoT (ESP32-based sensing & conveyor automation), and OCR (text extraction from product labels) into one seamless system.

Core Features (4-Part System)

  • Fruit Freshness Detection (Image Classification + ESP32)
    • Trained an ML model to classify Fresh vs Not Fresh
    • Real-time implementation using:
      • ESP32
      • DHT22 sensor
    • Dashboard options:
      • Capture new photos OR select from a folder
  • Product Detection + IR Conveyor Counting (YOLO)
    • YOLO model detects product on live camera
    • IR sensor + ESP32 counts items on a conveyor
    • Result: Real-time product tracking automation
  • OCR: Expiry Date / MRP / Manufacturing Details Extraction
    • YOLO detects the printed region
    • Code crops & extracts:
      • → MRP
      • → Expiry date
      • → Manufacturing details
  • OCR: Brand & Product Name Extraction
    • YOLO locates brand area
    • OCR extracts text
    • CSV maps brand to database (Product identification)

Components Used

  • Hardware
    • ESP32 microcontroller
    • IR sensor (for conveyor product counting)
    • DHT22 temperature & humidity sensor (fruit freshness logic)
    • Conveyor belt mechanism (DIY setup)
    • USB/Webcam for live product detection
  • AI / Machine Learning
    • YOLOv5 / YOLOv8 (for product detection + region detection)
    • Roboflow (dataset labeling & dataset preparation)
  • OCR / Text Extraction
    • EasyOCR / Tesseract OCR
    • CSV mapping for brand & product identification
  • Software / Development
    • Python (AI/ML + OCR scripts)
    • Jupyter Notebook (model training & testing)
    • VS Code (project folder & YOLO environment)
    • Arduino IDE (to program ESP32)

Working

Working

Code & Installation

The complete source code is available in the GitHub repository.
View on Github –>

My Learnings

  • Building ML pipelines (dataset → training → deployment)
  • YOLO dataset creation, training, and inference
  • OCR text extraction + region cropping using ML detection
  • Integrating AI + IoT (ESP32 + IR sensors)
  • Real warehouse automation workflow:
    • Detect → Identify → Extract → Count