Project Title:
Implementation of an OCR-Based Automated Cargo Number Recognition System on Kalmar Equipment
Objective:
To automate the process of capturing unique identification numbers printed on top of cargo containers using Optical Character Recognition (OCR) technology, thereby eliminating manual counting errors and improving operational efficiency at the port.
Implementation Process:
Hardware Integration:
. Three high-resolution CCTV cameras were installed on the Kalmar:
. Two cameras mounted on the Kalmar arms to capture the top-view container numbers.
. One camera placed below the driver’s cabin to capture lighting conditions for improved visibility.
Wiring & Connectivity:
. All wiring was completed inside the Kalmar using armored CAT6 cables and power cables to ensure durability.
. A POE switch and inverter were used to manage power distribution and camera functioning.
. An edge computing device (Asus NUC) was used for local OCR processing of images.
Tracking & Automation:
. A UPN (GPS) device was installed to monitor Kalmar’s real-time location.
. A router and NVR enabled live monitoring and recording.
. Images and data were stored locally using the NVR and 1TB hard disk, with backup on the Sandisk 32GB card.
System Testing & Validation:
. The system was thoroughly tested under varying lighting and environmental conditions.
. The OCR solution successfully captured and interpreted container numbers in both day and night scenarios.
Outcome:
> Successful automation of cargo number recognition, eliminating manual intervention.
> Enhanced data accuracy and operational efficiency in port logistics.
> Improved traceability through GPS-enabled location logging of container handling equipment.
> The project was completed on time and within scope, earning client appreciation.
Conclusion:
This project is a prime example of intelligent logistics automation using OCR and edge computing. The successful deployment on Kalmar demonstrates how traditional port operations can be modernized to meet Industry 4.0 standards. It stands as one of the most effective OCR-based automation systems in Indian port logistics to date.
Challenges:
- Accurately capturing cargo numbers in both day and night conditions.
- Mounting and integrating multiple devices on a moving Kalmar crane.
- Ensuring reliable power supply, network connectivity, and location tracking in a harsh port environment.
- Streamlining data capture, storage, and real-time visibility for logistics operations.
Materials & Hardware Used:
. 4U Enclosure Box
. Hikvision 8-Port POE Switch
. 4×4 Outdoor Waterproof Junction Box
. 3-Core Power Cable
. D-Link Armoured CAT6 Cable
. Keyboard / Mouse
. Sandisk 32GB Memory Card
. Asus NUC (Edge Device)
. Hikvision CCTV Cameras (3 units)
. Router
. HIKVISION 4-ch 1U K Series . AcuSense 4K NVR
. 1 TB Hard Disk
. Power Converter
. Inverter
. GPS/UPN Device
Future Enhancements Proposed:
Installation of a dash camera inside the driver’s cabin to monitor driver behavior and cabin activities for improved safety and accountability.
Conclusion:
This project is a prime example of intelligent logistics automation using OCR and edge computing. The successful deployment on Kalmar demonstrates how traditional port operations can be modernized to meet Industry 4.0 standards. It stands as one of the most effective OCR-based automation systems in Indian port logistics to date.