OCR and AI in Cargo Tracking: Real-Time Visibility

The worldwide air freight market will reach USD 133.47 billion, a considerable increase compared to USD 66.6 billion in 2021. The annual growth rate is projected to be 10.3%. With such growth in the volume of transported goods, automatization will shift from luxury to everyday necessity. In this article, we shall discuss how optical character recognition technology can merge with AI to eliminate manually held operations in the cargo industry. You’ll learn:
  • The benefits of the AI-powered OCR software for cargo tracking

  • Real-industry examples

  • Prospects of OCR and AI in cargo tracking

What are the unique challenges involved in cargo tracking?

Product delivery involves a complex chain of operations and collaborations of numerous companies with a lot of data entered manually.

Properly identifying shipped goods at every delivery stage is one of the most critical challenges in the freight forwarding industry. The shipped goods come in the majority of packs, boxes, and containers with different identification marks.

Shipping containers, for example, come with unique alpha-numeric numbers consisting of four letters and seven numerals. Previously, their identification, registration, and reporting were made manually, which was slow, labor-consuming, and prone to errors. Errors, in their turn, lead to containers being lost or sent to the wrong destination.

To respond to the growing demand for shipping, freight companies need better ways to identify and track shipments. At this point, the OCR technology comes into play.

The state of OCR software for cargo tracking: Overview

Optical character recognition is a data extraction technology from images, handwritten, printed, or typed documents or screens. OCR software captures, extracts, and structures data before bringing it to a digitized format. Image alt name: Optical character recognition for trace-and-tracking, Source: Sick.com In logistics, OCR software is used for numerous purposes, like extracting data from landing bills, processing invoices, or scanning parcel labels. World-renowned brands already utilize OCR solutions in their operations. Below, we describe several examples.
Use case 1: Nivea and Klippa
Nivea partners with Klippa for enhanced delivery handling. The OCR-based product by Klippa scans parcel labels to extract and structure information like order dates, parcel numbers, tracking numbers, etc. The amount of information from the label to be digitized is customizable and can be set according to a business goal. So that a shipment provider can read the tracking number only if needed.
Image alt name: Processing label with OCR tools Source: Klippa

Use case 2:

Delivery Hero and Nanonets: the multinational food delivery company partners with Nanonets to reduce manual document processing by 90%. The OCR tool from Nanonets gets integrated into the client company’s inventory-tracking system to enhance visibility at every shipping stage.

Use case 3:

Midsona and Sick: The organic food producers at Midsona, Germany, have implemented high-performance, optical character recognition from SICK for label inspection. Machine operators easily program the cameras for various purposes. For example, they can read tracking paths of products within a specific use-by-date span. The companies described above already use OCR tools enhanced by AI technology, as mere tracking is no longer enough. Below, we’ll discuss how the merge of OCR and AI helps overcome logistic tracking challenges and enhance the visibility of the product delivery status in real time.

OCR and AI: Enhancing OCR possibilities

Cargo tracking has specific limitations for conventional OCR software. For example, most document readers often mix characters and letters, which is a huge issue when it comes to reading multi-character parcel numbers consisting of numerals and letters. Mixing up “0” as a numeral and “O” as a letter can bring about undesired consequences. This is an excruciating point in processing alpha-numeric container codes, where the difference between “S” and “5” bears enormous importance.

Another challenge is reading rare languages, complicated alphabets, or rare fonts. With the growing globalization of the cargo market, that can be an issue.

The development of natural language processing and deep learning has changed how the OCR tools work. Instead of capturing every character as a separate image, contemporary OCR tools try to read the text in the context, therefore making more correct interpretations.

This increases the accuracy level of shipment tracking. Moreover, when the automated data capture is simpler, the reporting of shipment status can become more detailed. For example, a user receives updates on where in the terminal a parcel is and the current status.

The merge of logistics OCR applications and AI lets us go beyond optical character recognition and shift in intelligent data capture. For example, a solution from SICK Sensor Intelligence is geared towards detecting hazardous goods, such as radioactive, biohazardous, toxic, or allergenic goods. The solution that presents the merge of OCR and AI is able to read, identify, and report warning signs regardless of the label design.

Such a technology enables smarter logistics chains. For example, a shipping provider gets the shipping status of hazardous products in a separate list for better visibility.

The application of AI and further technology development will change the way we track products even more.

The future applications of the OCR for cargo tracking

OCR and Industrial Metaverse: In the metaverse, the physical and virtual industries merge, forming three-dimensional virtual spaces. The industrial metaverse opens huge opportunities for businesses to cut on labor and cost-consuming processes. For example, at virtual fairs, huge packs of goods or machines can be displayed and traded by presenting their digital twins only. This will eliminate the need for transportation to the fairs so they can be transferred directly to the end user. OCR technology can be applied for label scanning and extracting information from the virtual packs, like in real life.

OCR and augmented reality: AR has already merged with the OCR scanners within the industry to enhance tracking, reporting, and troubleshooting. Such merge, for example, lets a user see the contents of the pack by scanning a serial number or get a visual alert on damage, which could be otherwise skipped.

OCR and deep learning: the further use of deep learning lets us create solutions that will be more dependent on learning than programming. It will make it easier to reprogram existing features and will make OCR tools more flexible. For example, it will be possible to ask a tool to group the scanned products by size, type of product, or delivery destination, giving a voice command that will make OCR scanners similar to applications using Apple’s Siri or Amazon’s Alexa.

Summing up

The ability of error-free automated data capture enhances the way we track shipments. A modern client has almost unlimited possibilities for checking updates on delivery status thanks to the merge of OCR and AI. This new tech advancement is promising to bring us even more benefits in the future.

Interested to see how your business can benefit from the AI-based OCR technology?

Contact eNest for a free consultation and advice. Our team will provide you with an in-depth analysis of your business case, offer a possible solution, and provide a clear calculation of the project’s cost and timeline. Book a call now!


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Jagdeep Chawla

MS in Data Science
NorthWestern Univeristy, Illinois

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