Glossary

Object recognition refers to the automatic identification and localization of objects based on image, sensor, or measurement data. It enables the reliable differentiation of various objects and the digital capture of their properties.

In logistics, object recognition is used in combination with cameras, LiDAR, laser, and AI systems to automatically detect packages, pallets, containers, or hazardous material labels. The information obtained supports processes such as dimensioning, sorting, quality control, and material flow control. Modern algorithms enable fast and reliable recognition even at high conveyor speeds and under changing environmental conditions.

Practical example:
An AI-powered camera system automatically detects cartons, plastic wrap packaging, and hazardous materials labels on a conveyor system and transmits the results to the warehouse management system.

See also: Image processing, camera sensors, AI-powered object recognition, LiDAR, sensor technology, 3D sensors, free-form recognition, laser triangulation

OCR (Optical Character Recognition) refers to a process for automatically recognizing and digitizing printed or handwritten characters from images or documents. It converts text information into machine-readable data, enabling its further digital processing.

OCR is used in logistics to automatically read address labels, shipping documents, delivery slips, or serial numbers. When combined with camera systems, image processing, and AI, information can be captured quickly and immediately transferred to ERP, WMS, or transportation management systems. This reduces manual data entry, speeds up processes, and improves data quality.

Practical example:
At a parcel sorting center, an OCR system automatically reads the recipient’s address and tracking number from a damaged shipping label and transmits the data to the sorting system.

See also: Barcode scanners, image processing, data capture, camera sensors, AI-powered object recognition, object recognition, OCR-B, shipment tracking

OCR-B is a standardized font developed specifically for automatic optical character recognition. It enables machines to reliably read characters and supports the automated processing of information.

OCR-B is frequently used for machine-readable labels, documents, and markings. In logistics, this technology supports the automatic capture of information from labels, shipping documents, and shipping manifests. When combined with scanners, camera systems, and image processing, characters can be read, enabling digital processes without manual input. As a result, OCR-B improves the efficiency and reliability of data capture.

Practical example:
A document scanner recognizes OCR-B text on a shipping document and automatically transfers the captured information to the connected logistics system.

See also: Image processing, Data capture, Documentation, Document handling, Identification, OCR (Optical Character Recognition), Scanner, Shipping label

The OIML (Organisation Internationale de Métrologie Légale) is an international organization dedicated to harmonizing legal requirements for measuring instruments and measurement procedures. It develops globally recognized recommendations to promote the comparability and reliability of measurements in international trade.

The OIML publishes technical guidelines and testing requirements for measuring instruments such as scales, dimensioning systems, and other measuring instruments subject to calibration. Many national and international regulations are based on these recommendations or adopt them as the basis for approval and conformity assessment procedures. In logistics, the OIML supports uniform measurement standards and builds confidence in the accuracy and legal certainty of measurement data.

Practical example:
A manufacturer develops a new DWS system in accordance with OIML recommendations to meet the requirements for international approvals and global use.

See also: Calibratability, Measurement and Calibration Act (MessEG), Measurement and Calibration Ordinance (MessEV), MID (Measuring Instruments Directive), MID-compliant, NTEP (National Type Evaluation Program), PTB (Physikalisch-Technische Bundesanstalt), Certification

OIML R 129 is an international OIML recommendation for measuring instruments used to determine the dimensions of goods. It specifies the metrological and technical requirements to ensure that dimensioning systems provide reliable and legally compliant measurement results.

OIML R 129 defines test procedures, accuracy classes, and performance requirements for static and dynamic dimensioning systems. It serves as a basis for manufacturers, testing laboratories, and regulatory authorities to evaluate and approve such measuring instruments. In logistics, compliance with OIML R 129 is particularly important for DWS systems, whose measurement values are used for freight billing, shipping, or other business purposes.

Practical example:
A manufacturer has a dimensioning system tested in accordance with OIML R 129 so that the recorded dimensions can be used in compliance with legal requirements for international freight billing.

See also: Dimensioning systems, DWS, traceability, Measurement and Calibration Act (MessEG), MID (Measuring Instruments Directive), NTEP (National Type Evaluation Program), OIML, certification

Omnichannel logistics involves the integrated management of goods flows across various distribution and sales channels. It connects brick-and-mortar retail, online retail, and other sales channels into a uniformly managed logistics structure.

In this process, inventory, orders, warehouse processes, and transportation operations are coordinated across all channels. Digital systems such as ERP, WMS, and TMS solutions enable transparent inventory management and flexible order fulfillment. Automated processes, real-time data, and intelligent warehouse control support rapid delivery of goods and improve responsiveness to varying customer requirements.

Practical example:
A customer orders a product online, which is automatically selected from the most suitable available inventory, picked, and shipped via the appropriate shipping method.

See also: Digitalization, ERP system, Intralogistics, Warehouse Management System (WMS), Material flow, Supply Chain Management (SCM), Transport Management System (TMS)

Order picking refers to the process of assembling goods in accordance with a customer order or production requirements. Its purpose is to ensure that the required items are provided in full and in the correct quantities for subsequent shipment or production.

Picking can be performed manually, semi-automatically, or fully automatically and is often supported by warehouse management and pick-by systems. Accurate master data entry and the unambiguous identification of items ensure error-free processes and short processing times. In modern logistics centers, picking is a key process for increasing efficiency, quality, and delivery reliability.

Practical example:
An automated warehouse system retrieves the items required for a customer order, while the warehouse management system directs employees along the optimal picking route.

See also: Intralogistics, Warehouse Management System (WMS), Material Flow, Pick-by-Light, Pick-by-Voice, Master Data Entry, Goods Flow

An overhang refers to a package or load that protrudes beyond the permissible dimensions of a load carrier, pallet, or mode of transport. It can compromise safety, handling, and the smooth flow of logistics processes.

Overhangs are caused, for example, by incorrectly positioned goods, uneven loading, or unsuitable packaging. In automated logistics systems, they are detected and evaluated using cameras, LiDAR, laser scanners, or DWS systems. Early detection prevents collisions with conveyor systems or racking systems and helps ensure compliance with transportation and safety regulations.

Practical example:

Before storage, an automated inspection system detects a pallet’s lateral protrusion and diverts it for manual rework.

See also: DWS, free-form detection, contour inspection, load securing, LiDAR, object detection, pallet, protrusion

Oversized goods are defined as goods or shipments that cannot be processed through standardized logistics processes due to their size, shape, weight, or special characteristics. They often require specialized transportation, storage, or handling solutions.

Oversized goods can include, for example, oversized boxes, furniture, machine parts, or irregularly shaped items. Since these shipments often deviate from standard dimensions, precise measurement and reliable detection of their contours are particularly important. Modern 3D sensors, DWS systems, and AI-supported methods help automatically detect bulky goods, determine suitable transport routes, and prevent disruptions in the material flow.

Practical example:
In a parcel sorting center, an automated measurement system identifies an oversized package as bulky cargo and directs it to a separate processing station.

See also: DWS, cargo measurement, free-form recognition, irregular items, contour inspection, object recognition, protrusions, volume measurement