./systems › Industrial OCR Inspection System
⬡ AI & Data

Industrial OCR Inspection System

Domain
AI & Data Systems
Stack
8 Technologies
Status
In Progress

01 / The Problem

Pharmaceutical packaging lines require verification of printed batch numbers and expiration dates to ensure human readability and regulatory compliance. Manual inspection is slow, error-prone, and does not scale to line speeds above 200 units per minute.

02 / The Solution

An AI-powered optical character recognition system using a fine-tuned ONNX model deployed on an edge device connected to an industrial camera. The system performs real-time text detection, character recognition, and validation against the production batch database — triggering a reject actuator for non-conforming units without interrupting line throughput.

04 / Architecture

Industrial Camera (2MP, 500fps) ↓ Image Capture & Preprocessing (brightness norm, perspective correction) ↓ ONNX OCR Detection Model (PaddleOCR-based, int8 quantised) ↓ Character Recognition & Parsing ↓ Validation Engine (compares against production DB) ↓ ┌──────────────┬────────────────┐ │ PASS │ REJECT │ │ Continue line│ Actuator signal│ └──────────────┴────────────────┘ ↓ Event Logging & Dashboard

05 / Results

Deployed at production speed of 220 units/minute with 99.7% character recognition accuracy. Reduced QC personnel requirement from 3 to 1 per shift. Zero regulatory non-conformities recorded in the first 6 months of operation.

03 / Tech Stack
Python ONNX PaddleOCR OpenCV Industrial Camera Edge Device PostgreSQL FastAPI
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