INTRODUCTION TO INDUSTRIAL CODE RECOGNITION
At Adaptive Recognition, our industrial code recognition developments are related to identifier codes used in the traffic and logistics industries. All these software modules include OCR (Optical Character Recognition) algorithms, developed for a specific purpose: digitalizing these codes on the images or videos captured by cameras installed at roads, logistic centres, railways and other industrial environments.
The usage of such modern identification system offers a wide range of benefits, included but not limited to automating and simplifying road, railway, or harbor operations, aiding border control procedures, efficiently managing inventories, and utilizing surveillance systems.
Types of Industrial Code Recognition Software We Develop
Automatic Container Code Recognition – ACCR
The CARMEN® Automatic Container Code Recognition (ACCR) software has been specifically designed to extract and read the container codes of ISO containers, namely the primary identification number of intermodal (shipping) containers. This code identifies the owner and the type/category of the container, and it serves as a unique serial number.
Reading the ISO 6346 (BIC code), ILU, and MOCO container codes of shipping containers can automate and simplify road, railway, or harbor operations, help border control, manage inventories, and run container surveillance systems.
Automatic Dangerous Goods Sign Recognition (ADR)
The CARMEN® Automatic Dangerous Goods Recognition (ADR) software has been developed to recognize the Hazard Identification Numbers (HIN or Kemler codes) of vehicles carrying hazardous materials. The ADR code recognition in a traffic monitoring system increases safety on roads, bridges, tunnels – wherever hazardous materials are transported.
CARMEN® ADR identifies materials in transport through HIN codes that indicate primary and secondary hazards, which gives emergency responders the ability to quickly reference critical information about potential dangers.
US Truck Code Recognition (USDOT)
The CARMEN® DOT software has been created to extract and read the DOT number of a CMV (Commercial Motor Vehicle). All commercial vehicles in the United States require to have a unique identification number, the USDOT/DOT number, obtained from their respective Department of Transportation. The CARMEN® US DOT reader is a highly accurate tool for automatic identification and tracking, as well as supporting inventory control systems.
Railway Wagon Code Recognition (UIC)
The CARMEN® Railway Code Recognition software (UIC) automatically extracts and reads the UIC numbers from railway wagons. Much like commercial motor vehicles and ISO containers, railroad cars carrying freight or passengers also have unique and internationally standardized identification numbers.
Railway companies and logistics operations can significantly benefit from implementing UIC code recognition to read railroad car codes from an image or video signal. CARMEN® offers the highest UIC code recognition accuracy on the market.
Structure of a Typical Code Recognition System
Code identifier systems, no matter if they are installed on a highway or a harbor, usually include three main components:
- the camera that captures the identifiable object,
- the software that recognizes the code,
- and a backend system or database that manages all collected data.
The process starts with a camera; it can be a regular CCTV camera or also purpose-built traffic or container camera. For the best input, we suggest using purpose-built cameras that are also available at Adaptive Recognition.
It is crucial for successful code recognition that the code on the photo or video is not blurred, over-exposed, or too dark, and the contrast is appropriate. The captured images or videos will be the input of the code recognition software. Better quality input will mean a higher chance of successful recognition.
The code recognition can be performed either locally – on the camera or externally, near the camera – or remotely on a server. CARMEN® can be used in both applications.
When the recognition is done, the data is usually transferred to an application or saved in a database for further processing.