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Text Scanning Tire Sidewall

How AI Scanner Help Capture TIN and DOT Numbers to Enable Smarter Tire Tracking

In 2020, A Tire Company recalled over 90,000 tires due to potential belt separation. The recall listed partial DOT codes, forcing fleet operators and dealers to manually inspect thousands of tires scrubbing faded sidewalls, jotting TINs on paper, and verifying records by hand.

This isn’t a one-off event. Tire recalls affect millions of units every year, yet most businesses still rely on manual TIN/DOT tracking, leading to slow response times, grounded vehicles, and compliance risks.

Why TIN and DOT Codes Matter More Than Ever

TIN and DOT numbers are more than regulatory labels they are essential for tire traceability, asset identification, and recall readiness. The U.S. National Highway Traffic Safety Administration (NHTSA) reports that a large portion of tire recalls are traceable only through accurate DOT codes.

Without efficient tire identification, service providers and fleet managers face real risks: delays, data errors, and overlooked recalls that can affect safety and compliance.

The Problem: Manual Tire Data Capture Slows Everyone Down

The issue isn’t availability of tire data it’s that manually reading and recording TIN and DOT codes is inefficient, especially on dirty, curved, or worn sidewalls. In fast-paced environments like automotive service centers, tire yards, and distribution depots, expecting technicians to write down codes or use spreadsheets simply doesn’t scale.

One missed or misread code could allow a recalled tire to remain in service risking both safety and regulatory violations.

The Solution: AI Mobile SDK for Tire Identification

Modern solutions like AI scanner mobile SDKs are transforming how tire identification is done. Instead of relying on manual transcription, service teams can now scan tire sidewalls using AI-powered mobile tools that automatically capture TIN and DOT codes even in poor lighting or on aged tires.

The Scanflow Asset Identification Mobile SDK allows OEMs, fleet operators, and service providers to embed intelligent scanning into their own apps or workflows. With it, teams can identify tires instantly and digitally enabling faster recalls, more accurate audits, and smarter inventory tracking without any paperwork.

Conclusion

TIN and DOT codes might be small, but they carry enormous weight when it comes to safety, traceability, and operational efficiency. By using Asset Identification Mobile SDK, automotive businesses can eliminate human error, respond to recalls faster, and make every tire inspection count.

Is your team still relying on eyes and clipboards to track tire safety?

Discover how Scanflow AI can help digitize and automate tire inspections from TIN/DOT scanning to instant digital logging. Request a demo or get in touch

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How AI is Transforming Data Capture Across Industries

In today’s fast-paced world, businesses are turning to AI for data capture to collect, process, and manage complex information with greater accuracy and speed. This technology extends beyond simple data extraction, it efficiently handles alphanumeric data like VIN plate scanning, tire sidewall numbers, serial numbersBar codes, QR codes etc., ensuring precision even in the most challenging conditions. 

Industries such as automotive, logistics, manufacturing, and retail are integrating AI visual inspection solutions and AI for data capture to streamline workflows, reduce manual errors, and enhance operational efficiency. This shift isn’t just a technological upgrade but it’s redefining how businesses capture and use data in real time.

  1. Expanding Data Capture Beyond Traditional Methods

AI is revolutionizing data capture by automating the collection of complex information across various environments. What once required manual data entry or specialized equipment can now be handled seamlessly by AI visual inspection workflows. 

Key applications of AI in advanced data capture include: 

VIN Plate Scanning: Essential in the automotive and logistics industries, AI quickly and accurately captures vehicle identification numbers, streamlining tracking, registration, and inventory. 

Tire Sidewall Numbers: AI can extract detailed information from tire sidewalls, supporting product verification, recall management, and quality assurance in manufacturing. 

Serial Number Scanning: Useful in electronics and industrial sectors, AI captures serial numbers for inventory tracking, warranty management, and equipment identification. 

Alphanumeric Text Recognition: AI retrieves data from labels, machinery plates, and industrial documentation, ensuring error-free data input and reducing manual workload.

Why it matters?

AI captures data from worn, distorted, or low-visibility surfaces, ensuring accurate collection under challenging conditions. This reduces errors, enhances data consistency, and allows businesses to maintain accurate records without manual oversight. 

  1. Automating Workflows for Greater Efficiency

Manual data entry is labor-intensive and prone to mistakes. AI introduces automation across industries, reducing the need for human intervention while improving accuracy and speed. This is particularly valuable in environments requiring high-volume data capture. 

Benefits of AI-automated workflows include: 

Faster Data Processing: AI captures and processes large volumes of data in seconds, accelerating operations in industries like logistics and automotive. 

Error Reduction: AI eliminates human error by accurately reading and recording alphanumeric information, even from difficult angles or damaged surfaces. 

Seamless Workflow Automation: AI integrates with existing ERP systems, warehouse management software (WMS), and supply chain platforms to automate data transfer and reporting. 

Industries such as manufacturing and retail benefit significantly from automated workflows, as AI can track incoming and outgoing shipments, verify product details, and ensure smooth operational transitions. 

  1. AI for Data capture: From Capture to Insight

AI doesn’t just capture data. Once collected, the data is processed, organized, and integrated with existing systems to provide a comprehensive view of business operations. 

Applications of AI for data management across industries include: 

Inventory Control: In warehousing and logistics, AI tracks product movement and ensures real-time updates, reducing miscounts and stock discrepancies. 

Automated Audits: AI facilitates internal audits by automating the collection and verification of critical data, ensuring compliance with industry regulations. 

Data Accuracy and Reporting: AI improves reporting precision by capturing real-time data across multiple touchpoints, enhancing supply chain visibility and operational transparency. 

By automating data management, businesses can process vast information efficiently while maintaining accuracy, ultimately enhancing decision-making and optimizing resource allocation. 

  1. Industry-Specific Use Cases of AI in Data Capture

AI’s adaptability makes it invaluable across multiple sectors, where it enhances efficiency and reduces human error. Here’s how AI-driven data capture transforms different industries: 

Automotive Industry: AI simplifies VIN plate scanning, improves inventory accuracy, and facilitates compliance tracking across vehicle fleets. 

Logistics & Supply Chain: AI automates serial number scanning for package tracking, delivery validation, and warehouse automation, ensuring faster and more accurate logistics. 

Manufacturing: AI captures tire sidewall numbers and other industrial identifiers, streamlining product lifecycle management and enhancing production efficiency. 

Retail & Consumer Goods: AI supports large-scale inventory tracking and customer data management, improving efficiency in managing supply chains and retail stock. 

Healthcare: AI assists in capturing device serial numbers and alphanumeric codes on medical instruments, ensuring accurate records for regulatory compliance. 

The Future of AI in Data Capture

The future of AI visual inspection solutions is poised to bring even more advanced capabilities to data capture and management. With ongoing advancements, AI will offer: 

Improved Recognition Accuracy: Enhanced models for capturing data from irregular surfaces, low-light environments, and damaged labels. 

Integrated Systems: Seamless communication with broader digital ecosystems, including IoT devices and smart supply chains. 

Scalable Automation: Greater scalability for industries handling high volumes of alphanumeric data, ensuring accuracy across diverse applications. 

Conclusion: AI is Redefining Data Capture 

AI is transforming the way industries handle data capture, moving beyond traditional methods to deliver faster, more accurate, and automated solutions. From VIN plate scanning in the automotive sector to serial number scanning in logistics, AI enhances efficiency and reduces human error across various touchpoints. 

By integrating AI visual inspection workflows and AI for data capture, businesses can automate complex processes, improve operational accuracy, and gain real-time insights. This shift not only optimizes resource allocation but also ensures smoother, more efficient workflows across industries. 

As AI continues to evolve, its applications in data capture will expand, offering smarter, more scalable solutions that drive innovation and operational excellence. Embracing these advanced technologies is no longer optional, it’s essential for businesses looking to stay competitive in a data-driven world.

To stay ahead of this curve, Explore Scanflow AI and see how it can benefit your business operations, visit – https://www.scanflow.ai/get-in-touch/

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