Category: Barcode scanning

Over 10 billion barcodes are scanned every single day, twice as many as a decade ago. Behind every one of those scans is a simple question that has quietly become enormously consequential: did the scanner get it right? In a 200,000-unit-per-day fulfillment center, a 2% drop in scan accuracy results in 4,000 extra scans per day, consuming 50–70 wasted labor hours. In a pharmaceutical warehouse, a single misread could mean the wrong medication reaching the wrong patient. On a manufacturing line running at 120 parts per minute, a 95% read rate costs the equivalent of 6 unprocessed parts per minute. 

For decades, the answer to barcode scanning challenges was hardware: bigger lasers, better optics, sturdier guns. Traditional scanners served the industry reliably in a simpler world: clean labels, flat surfaces, controlled lighting, and narrow barcode formats. Today’s operational reality looks nothing like that. Labels arrive dented, smudged, and curved. Workers scan at angles. Warehouses operate 24 hours a day under variable lighting. Formats multiply: 1D, 2D, QR, Data Matrix, DPM codes on metal parts. The traditional scanner increasingly struggles to keep pace. 

Enter AI-enabled barcode scanning: a fundamentally different approach that uses computer vision and deep learning to understand images rather than reflect lasers off lines. This blog cuts through the marketing noise to answer the real question: what actually changes when you move from traditional to AI scanning, and when does the upgrade genuinely matter? 

A market at an inflection point 

The barcode scanner market is growing steadily, but its composition is shifting fast. The global market was valued at approximately USD 7.4 billion in 2024 and may reach USD 13.0 billion by 2033, growing at a CAGR of around 6–10% across segments. The forecast for industrial barcode scanners alone may grow at a 11.3% CAGR through 2034, driven by Industry 4.0 adoption, e-commerce fulfillment pressure, and regulatory traceability requirements.

MARKET SIZE
Barcode scanner market valued at USD 7.4 billion in 2024

Projected to reach USD 13.0 billion by 2033 at 6.13% CAGR with AI-integrated scanning driving the fastest growth segment (IMARC Group, 2024) 

The technology mix within that market is shifting dramatically. Imaging scanners, which capture full barcode images rather than sweeping a laser line, now account for the majority of new device shipments since 2022, while laser scanner demand is actively declining in retail and office settings. And on top of imaging, AI-powered decoding is being layered in at increasing speed: Cognex launched its AI-powered DataMan 290 and 390 barcode readers in January 2025; Datalogic introduced new AI-embedded solutions at NRF 2025 the same month; and in March 2025, DHL and Zebra Technologies announced a strategic partnership to deploy enterprise-grade AI scanning across DHL’s global network. 

What is driving this shift is not marketing fashion. It is the real operational gap between what traditional scanners can and cannot do and what AI systems handle without breaking stride. 

How traditional barcode scanning works and where it breaks down 

The mechanics of traditional scanning 

Traditional barcode scanning, whether laser-based or early-generation CCD/imager, operates on a fundamentally optical-mechanical principle. A laser scanner sweeps a laser beam across the barcode, measuring the reflection pattern of dark bars and white spaces to decode the encoded data. This approach is fast, reliable, and inexpensive for its purpose: reading clean, well-printed, properly oriented 1D barcodes in controlled conditions. 

CCD (Charge-Coupled Device) scanners took a step forward by capturing ambient light patterns from the barcode surface rather than emitting a laser, offering better performance on some surface types. First-generation imaging scanners added camera sensors to capture full barcode images, enabling 2D code reading. But even these systems rely on rule-based decoding algorithms and fixed mathematical logic to interpret whatever the optics capture, without the ability to adapt, learn, or reason about ambiguous inputs. 

The core limitations that AI is solving 

Traditional scanning has a well-documented set of failure modes that become critically costly at scale: 

• Angle and Alignment Dependency: Traditional laser scanners require the laser line to hit the barcode at close to 90 degrees. Misalignment causes failed reads. In pick-and-pack operations where workers scan at varied angles and distances throughout a shift, this demands constant repositioning, slows throughput, and leads to fatigue-driven errors. 

• 1D Format Lock-In and Surface Problems: Laser scanners are famously incompatible with inverted barcodes and poorly equipped for barcodes on reflective, curved, or uneven surfaces. Manufacturing part numbers etched directly into metal (DPM codes) or printed on curved packaging are common casualties. 

• Damaged and Degraded Code Failures: Labels damaged during transit, partially obscured by shrink wrap, smudged by moisture, or faded through UV exposure routinely defeat traditional scanners. The scanner cannot reason about partial barcode data; it either reads or it doesn’t. 

• Inflexibility to Format Changes: Rule-based decoders optimize for specific symbology families. When a supplier introduces a new barcode format such as shifting from EAN-13 to Data Matrix or adding QR codes for a new product line existing scanners may fail to read them, forcing teams to replace the hardware. 

• Hardware Degradation Over Time: Traditional handheld laser scanners use moving-mirror assemblies and mechanical components. More moving parts mean more failure points. Normal wear from 50,000+ scans creates micro-scratches on laser exit windows, reducing read range by 15–25%. Most quality scanners have a lifespan of 3–5 years with proper maintenance. 

THE HIDDEN THROUGHPUT COST
A manufacturing line at 120 parts/min with 95% read rate loses 6 parts per minute.
A 2% accuracy drop in a 200,000-unit fulfillment center = 4,000 extra scans daily and 50–70 wasted labor hours. Organizations rarely track the cost of scan failures, yet it is always present. (Visionify / OxMaint, 2025).

A scanner operating at 92% accuracy still works, but it forces pickers to re-scan items multiple times, slows throughput by 8–15%, and introduces mis-picks when frustrated workers override the system.”  OxMaint, Barcode Scanner Maintenance for Fulfillment Accuracy, 2025 

How AI-enabled barcode scanning works and what it changes 

The architecture under the hood 

AI-enabled barcode scanning replaces rule-based optical decoding with deep learning models trained on vast datasets of real-world barcode images, including millions of examples of damaged, distorted, partially obscured, low-contrast, and awkwardly angled codes. The system does not simply measure reflected light patterns; it applies convolutional neural networks (CNNs) to analyze the full image, identify the barcode region, interpret ambiguous elements, and decode the data with contextual intelligence. 

This approach runs directly on the scanning device, whether a smartphone, tablet, ruggedized handheld, or fixed industrial camera, without requiring cloud connectivity for each scan. The model’s intelligence is embedded in the software SDK, enabling it to operate at full speed in offline, low-connectivity, or security-sensitive environments. 

The real-world differences that matter 

The differences between AI and traditional scanning are not marginal improvements in the same dimensions; they represent a shift in what scanning can fundamentally handle: 

• Damaged and Low-Quality Code Reading: Where traditional laser scanners struggle with out-of-focus images (achieving around 10–13% read rates for standard open-source engines on challenging images), leading AI scanning SDKs achieve 81–92% read rates on the same degraded inputs. AI engines read damaged or low-quality barcodes measurably faster, with fewer missed scans and fewer manual corrections. 

• Multi-Symbology Flexibility: AI barcode scanners work across all major 1D and 2D symbologies simultaneously, without requiring reconfiguration—QR codes, Data Matrix, PDF417, Code 128, EAN, UPC. The system identifies and decodes whatever it encounters. No hardware swap required when a supplier changes format. 

• Hardware Agnosticism (Run on Existing Devices): AI scanning software runs on any camera-equipped device, including existing smartphones, tablets, ruggedized Android handhelds, and industrial fixed cameras. Enterprises deploy enterprise-grade scanning capability on devices workers already carry, eliminating the need for a dedicated scanner estate for many use cases. 

• Context-Aware and Intent-Driven Scanning: Next-generation AI systems move beyond simply reading the barcode to understanding why the scan is happening. Context-aware scanning identifies which barcode a user intends to scan when multiple codes are present in the camera frame, reduces accidental scans, and integrates workflow intelligence into the scanning action itself. 

• Batch and Multi-Barcode Capture: AI scanning systems can process multiple barcodes simultaneously within a single camera frame, accelerating pallet receiving, multi-item picking, and high-density storage scanning tasks that would require multiple triggers pulls with a traditional handheld. 

• Seamless ERP and WMS Integration: Because AI scanning runs on software SDKs with documented APIs, it integrates directly with WMS, ERP, and MES systems without requiring specialized middleware or hardware drivers. Data flows from scan to system record in real time. 

SPEED AND ACCURACY BENCHMARK  AI scanning engines process up to 500 barcodes per minute with 99%+ accuracy.  On challenging out-of-focus images: AI leaders achieve 79–92% read rates vs. 10–14% for standard open-source engines (Anyline / Dynamsoft benchmark, 2025)

AI vs Traditional Scanning: Head-to-Head Comparison 

The differences between the two approaches play out differently depending on the scanning environment and task. Here is a direct comparison across the dimensions that matter most operationally:

 

Capability	Traditional Scanning	AI-Enabled Scanning
Damaged/degraded barcodes	Often fails or requires a re-scan	Reads using pattern inference and ML
Code formats supported	Usually 1D laser; limited 2D	All major 1D, 2D, QR, DPM simultaneously
Surface types	Flat, clean labels only	Curved, reflective, uneven, direct-part marks
Scanning angle tolerance	Narrow - requires alignment	Wide - any angle, any orientation
Hardware requirement	Dedicated scanner device	Existing smartphone, tablet, or fixed camera
Learning/adaptation	None - fixed algorithm	Continuous ML improvement from scan data
Multi-barcode capture	One at a time	Multiple codes in a single frame
Integration	Hardware drivers, middleware	Software SDK with direct API to WMS/ERP
Context awareness	None - reads what it sees	Intelligent intent detection and error filtering
Total cost of ownership	Hardware + maintenance cycle	Software license on existing devices
Offline capability	Full (laser optics)	Full (on-device model, no cloud needed)

Scanflow: AI Barcode Scanning Built for Industrial Realities 

Among the AI scanning solutions designed specifically for enterprise and industrial environments, Scanflow has built its platform around the operational challenges that traditional scanners handle poorly: complex manufacturing parts tracking, logistics traceability, multi-format code reading across unpredictable conditions, and seamless integration into existing enterprise workflows. 

What Scanflow Does Differently 

Scanflow is an AI-powered scanning SDK designed to run on standard smart devices, smartphones, tablets, ruggedized handhelds, and wearables, delivering what the company describes as enterprise-grade intelligent data capture without requiring specialized hardware investment. The system trains its AI models to scan barcodes, QR codes, serial numbers, and text even in difficult real-world conditions, such as low-light environments, long-range distances, damaged labels, and varying orientations and angles. 

Rather than positioning itself as a generic barcode-scanning tool, Scanflow is purpose-built for industries where traceability is mission-critical: manufacturing, logistics and warehousing, automotive, and healthcare. Its capabilities include: 

• Barcode and QR code scanning across all major symbologies, including Data Matrix and Code 128 formats common in industrial part marking. 

• Serial number capture from product labels, enabling complete traceability from production line to end user, is a critical function for warranty management, recall response, and regulatory compliance. 

• VIN (Vehicle Identification Number) scanning and license plate recognition for automotive supply chain applications. 

• Tire sidewall scanning, including TIN (Tire Identification Number) capture from curved, embossed rubber surfaces, is particularly challenging and is beyond the capabilities of standard barcode scanners. 

• Integration via native Android and iOS SDKs, plus cross-platform support for React Native, Xamarin, and Flutter, with server-side API deployment for fixed-scanner setups. 

• Logistics-specific capabilities, including SSCC pallet label scanning, inbound dock validation, and shipment verification that update WMS records in real time. 

Where Scanflow Fits in the AI vs Traditional Decision 

Scanflow is most relevant to organizations facing the limitations of traditional scanning in complex traceability scenarios. If a business is managing serial-number-level product tracking across a supply chain, running operations where labels arrive damaged or in varied formats, or building mobile applications that need to capture data from non-ideal barcode conditions, Scanflow’s SDK approach offers a practical path to AI-grade scanning without a hardware overhaul. 

The key commercial proposition, deploying enterprise scanning capability on existing smart devices rather than maintaining a dedicated scanner estate, addresses one of the most common barriers to AI scanning adoption: upfront capital cost. Rather than replacing every traditional scanner on the floor with a new AI-capable device, organizations can extend AI scanning capability through software to the Android and iOS devices already in workers’ hands. 

“Scanflow’s AI-powered barcode scanning solution ensures precise and rapid data capture, streamlining inventory management and supply chain processes deployed on smart devices your teams already carry.”  Scanflow.ai  

When Does the Upgrade to AI Scanning Actually Pay Off? 

AI scanning is not universally superior for every use case. The investment calculus depends on the specific scanning environment and operational challenges. Here is a practical framework: 

Scenarios Where AI Scanning Delivers Clear ROI 

• High damage and degradation rates: If your operation regularly encounters damaged shipping labels, weathered barcodes, or labels obscured by shrink wrap or palletization, AI scanning eliminates the manual re-scan and re-keying cycle that costs labor time and introduces errors. 

• Multi-format barcode environments: Operations where suppliers, customers, and internal systems use different barcode symbologies, and a single worker or device must handle them all, benefit immediately from AI’s format-agnostic reading. 

• Mobile and field-based operations: When scanning happens outside fixed scan stations in field service, last-mile delivery, site audits, or mobile receiving, AI scanning SDKs on smartphones eliminate the need to carry and maintain dedicated handheld devices. 

• Traceability-critical workflows: For serial number capture, part-number tracking, tire ID scanning, or any process where scan accuracy is directly tied to warranty, compliance, or recall obligations, the accuracy uplift from AI scanning has direct financial value. 

• Scaling operations with tight labor budgets: When labor is scarce or expensive, the 8–15% throughput loss from legacy scanner degradation and re-scan cycles becomes a boardroom-level concern. AI scanning’s higher first-pass read rates directly translate into higher throughput and labor efficiency. 

Where Traditional Scanning Remains Adequate 

For high-volume, fixed-conveyor applications that read clean 1D barcodes in controlled environments, such as sortation lines in parcel hubs, laser scanners remain cost-effective and performant. The ROI of an AI upgrade depends on whether the failure modes described above are actually occurring in your operation. If first-pass read rates are consistently above 99.5% and formats are stable, the upgrade economics are less compelling. 

INDUSTRY ADOPTION SIGNAL  Imaging scanners account for the majority of new scanner shipments since 2022  Laser scanner demand is actively declining in retail and office settings as AI-enhanced imaging defines the new standard (Tera Digital / Market Analysis, 2025)

Three Trends Accelerating the Shift to AI Scanning 

1. The Traceability Regulatory Wave

Supply chain traceability requirements are tightening across the US, the EU, and the Asia-Pacific region simultaneously. The US Uyghur Forced Labor Prevention Act requires documented supply-chain provenance. EU Digital Product Passport regulations will require machine-readable lifecycle data for manufactured goods. Healthcare and pharmaceutical traceability mandates continue to expand. Each of these regulatory requirements creates a direct demand for scanning systems that can accurately capture serial numbers, product codes, and material identifiers at every point in the supply chain, exactly where AI scanning outperforms traditional approaches. 

2. E-Commerce Volume and Speed Pressure

Global e-commerce projection may exceed USD 8 trillion by 2030. The fulfillment operations supporting that volume are under intense pressure to scan faster, with fewer errors, across an increasingly diverse product assortment. Traditional scanning is a bottleneck in this environment; AI scanning with batch capture, angle tolerance, and multi-format reading directly addresses the throughput demands of modern fulfillment. 

3. The Smartphone as Enterprise Device

The rise of BYOD (Bring Your Own Device) and enterprise mobility strategies has placed powerful camera systems in the hands of every warehouse worker, delivery driver, and field technician. AI scanning SDKs like Scanflow’s transform those cameras into enterprise-grade scanning tools, fundamentally changing the cost structure of deploying scanning capability. Instead of a capital expenditure cycle tied to dedicated hardware refresh, organizations deploy scanning as a software license update. 

Conclusion

The gap between AI-enabled barcode scanning and traditional scanning is not a generational hardware upgrade; it is a categorical shift in what scanning can do. Traditional scanners ask: “Can I read this barcode under these conditions?” AI scanning asks: “What is the data here, and how do I get it reliably regardless of condition?” The difference matters every time a label is damaged, an angle is awkward, a format is unexpected, or a serial number capture is missing with legal-grade accuracy. 

For manufacturers managing component traceability, logistics operators building intelligent supply chains, and field teams relying on mobile devices to capture data in the wild, AI scanning is not an optional upgrade; it is the infrastructure that enables accurate, scalable data capture. Solutions like Scanflow that deliver this capability as an SDK deployable on existing devices, integrable with existing systems, and purpose-built for industrial complexity offer a pragmatic entry point that avoids the traditional choice between capability and cost. 

The 10 billion daily barcode scans of today will only grow. The question is not whether AI scanning will displace traditional methods in demanding environments; it is how quickly organizations will make the transition before the operational costs of legacy scanning accumulate beyond tolerance. 

Key Takeaways 

• Traditional barcode scanning relies on fixed, rule-based algorithms and struggles with damaged codes, variable angles, and multiple formats, resulting in 8–15% throughput losses in real-world conditions. 

• AI scanning uses deep learning on full barcode images, achieving 79–92% read rates on challenging inputs where traditional engines score 10–14%. 

• The shift from hardware-centric to software-centric scanning, running AI SDKs on existing smartphones and tablets, changes the economics of scanning deployment. 

• Scanflow’s AI SDK, built for traceability-critical scenarios where traditional scanners fail most: serial number capture, tire sidewall scanning, VIN reading, and multi-format industrial environments. 

• Regulatory pressure (UFLPA, Digital Product Passports), e-commerce fulfillment speed demands, and the smartphone-as-enterprise-tool trend are all accelerating the move to AI scanning. 

• The upgrade pays off most clearly in operations with high label damage rates, multi-format environments, field-based scanning, and processes where scan accuracy has direct compliance or warranty implications. 

Barcodes are integral for tracking items and providing identification in today’s world.

Barcodes allow an organization to quickly, accurately, and automatically capture data at retail store checkout, warehouse shelves, hospitals, and manufacturing operations. 

However, choosing the correct barcode can prevent scanning failures, regulatory issues, and operational delays, helping you feel confident in your decisions. This guide aims to support you in making informed, reliable choices for your company’s needs. 

 Differences between 1D and 2D Barcodes 

(Linear and Matrix Barcodes) 

In most cases, we can classify barcodes into two types: one-dimensional (1D) and two-dimensional (2D). 

1D BARCODES VS  2D BARCODES 

Feature	1D Barcodes	2D Barcodes
Data storage	Limited (numbers/characters)	High (text, URLs, IDs, metadata)
Structure	Horizontal lines	Square or rectangular patterns
Scan direction	Single direction	Multiple directions
Error correction	Minimal	Strong built-in error correction
Space efficiency	Requires more width	Stores more data in less space
Common use	Retail, logistics	Manufacturing, healthcare, mobile

Barcode Types and Commonly Used Barcodes  

Different industries use specific barcode types, and choosing the right standard directly affects scan accuracy, system integration, and scalability. 

One-dimensional (commonly referred to as 1D) barcodes (linear barcodes) have been the predominant barcode standard for many decades, typically found in retail, packaging, and logistics. 1D barcodes allow businesses to quickly and reliably identify products. The UPC is the most widely used 1D barcode in North America, while the EAN is the most commonly used standard internationally; both continue to serve as the underlying foundation of retail point-of-sale and inventory control systems. 

 Growing demand for rich data and flexible barcode scanners is pushing the market toward adopting 2D barcodes, which store more data in less space and are camera-readable. 

 This trend is occurring worldwide. Retail and manufacturing ecosystems are preparing to migrate from a world of 1D barcodes to hybrid 1D and 2D barcodes (i.e., QR Codes and Data Matrices) on packages. Instead of eliminating all 1D barcode usage, many organizations are pursuing hybrid strategies that will enable organizations to continue using existing infrastructure while moving towards future-proof capabilities. 

Curious about how many kinds of barcodes there are?

 Exploring this can inspire confidence in your ability to select the best options for your operations.  

While there are approximately 30 barcode symbologies in use today, only a handful still exist. We focus on the 13 primary types of barcodes used in retail, logistics, manufacturing, healthcare, and transportation industries. 

 One-Dimensional (1D) Barcode Types 

 1. UPC Barcode 

 UPC Barcodes are primarily used in North America (the United States) to identify and scan consumer items via point of sale. They provide quick, easy checkouts while still allowing a unique identifier for each item within a given retail system. 

The most common UPC format is UPC-A. This barcode type is a 12-digit number that uses the same encoding system as EAN-13, with the only major difference being that UPC-A omits the leading zero used in North America. 

When packaged products are smaller than traditional packaging, retailers use the UPC-E type. UPC-E is a compressed format that contains 6 digits and interfaces well with standard UPC systems. 

In addition to their use at the point of sale, UPC barcodes play an important role in retail inventory management by enabling accurate tracking from the manufacturer to the warehouse, from the warehouse to the distribution center, from the distribution center to the store, and finally from the store to the customer. 

 UPC Barcode Features: 

• Data Capacity: Global Trade Identification Number (GTIN) with a Capacity of 12 Digits for UPC-A and 12 for UPC-E. 
• Industry: Retail 
• Checksum: Modulo 10 Checksum 

2. EAN Numerical & Quantitative Identification System  

EAN barcodes (European Article Numbers) serve as an international standard for identifying retail products and function as a global counterpart to the American Universal Product Code (UPC). EAN barcodes originated in Europe; however, they have become the predominant form of product identification worldwide. 

 The EAN-13 is the most commonly used EAN barcode and encodes 13 numerical digits. The components of an EAN-13 barcode include a prefix (indicating the country/region), the manufacturer’s code, the product code, and a validation digit/checksum. The EAN-8 is an alternative to EAN-13 that is shorter and typically used for small retail items (to conserve space). 

The EAN standard helps maintain consistency across a global supply chain, enabling seamless scanning during the order process across various manufacturers and suppliers worldwide. 

 EAN Barcode Characteristics Overview-  

• Data capacity: 13 (EAN-13) and/or 8 (EAN-8) digit(s) (as can be encoded) 
• Industry Application: Retail or Consumer Goods 
• Checksum Verification: Uses mod10 to verify 
• Industry Standard: ISO/IEC 15420 
• Types of EAN: EAN-13, EAN-8 

3. Code 39

Code 39 is one of the earliest alphanumeric barcode symbologies and is still widely used by various industrial and enterprise companies. While both UPC and EAN use only numerical digits for their identification systems, Code 39 supports both numbers and upper-case letters (which makes it a good choice for internal tracking systems). 

Common uses of Code 39 include automotive manufacturing, defense, and logistics, where ensuring good readability and reliability take priority over using a small, compact design. Code 39 is often printed alongside human-readable text (underneath) to enable manual verification as needed. 

Due to their lower data density, Code 39 Barcodes are longer than those of many other coding standards, which limits their use on smaller labels. 

 Code 39 Features 

• Can be used to encode: Alphanumeric (A-Z, 0-9, Limited Characters) 
• Industries that use this Code standard include: Manufacturing, Automotive, Defense, and Logistics. 
• A Checksum: Optional 
• Code 39 Standard ISO/IEC 16388 
• Variants of Code 39: Standard Code 39, Full ASCII Code 39 

4. Code 128  

Code 128 is a linear barcode with a high data density. This bar code can encode more characters and/or more data in a compacted format. 

Code 128 supports the full ASCII character set, and dynamically switches between character subsets to maximize the amount of data that Code 128 can encode.  

These features make Code 128 an ideal barcode format for logistics operations, including shipping and receiving, as well as in warehouse settings where users need to encode both numeric and alphanumeric data efficiently. 

In fact, even though the primary use of code 128 outside of retail is as an identifying and tracking method for couriers and freight systems. 

Code 39 vs Code 128: Code 39 prioritizes simplicity and readability, while Code 128 prioritizes efficiency, compactness, and data capacity. 

Code 128 Overview Features  

• Can be used to encode: all 128 characters of the ASCII character set 
• Type of Industry that uses code 128: Logistics, Warehousing, Transportation 
• Code 128: Mandatory Checksum 
• Code 128: Standard ISO/IEC 15417 
• Variations of Code 128: Code Set A, B, C 

Interleaved 2 of 5 (ITF) is a Numeric-only Barcode extensively used throughout distribution to label cartons or packages. ITF is particularly well-suited for printing onto corrugated paper, where print quality may not be very controlled. 

The Intelligent Technology Foundation (ITF) is a method of encoding numeric data using pairs of values. This process enables efficient numeric representation of products while being durable enough for industrial use. ITF is typically seen on packages or pallets, rather than on individual consumer products.   

Here is an overview of what ITF is:   

• Data only: Numeric  
• Industries that use ITF: Packaging, warehousing, logistics  
• Checksum: Optional  
• Standard Code: ISO/IEC 16390  
• Variations: ITF-14   
  

5. Code 93

Code 93 provides a more compact and reliable alternative to Code 39. Code 93 uses a similar character set as Code 39, but with much more density and improved error detection and correction.  

While the overall use of Code 93 is not high, it has found limited use in logistics and in internal enterprise applications, particularly when an organization wants to use less space to represent a barcode than a standard 2D code would require.  

Code 39 Features:  

• Data stored: Alphanumeric  
• Industries that use Code 93: Logistics, Internal Tracking  
• Checksum: Required (2 Character)  
• Standard: ANSI MH10.8M  
• Variations: Standard Code 93  

6. Codabar  

Codabar is an older symbology still used in certain settings, such as libraries, blood donation facilities, and some legacy logistics systems. Codabar makes printing and decoding easy, with little to no computers required for either.  

While most barcode types have replaced Codabar, it still has a place in systems where companies require backward compatibility. 

Although updates to Codabar helped make it compatible with more contemporary barcode formats, it remains in use where backward compatibility with existing systems is necessary. 

CODABAR: Overview of Characteristics 

• Data Capability: Numeric, includes a limited number of symbols. 
• Industries Utilized by the Code: Libraries, Health Care, Legacy Systems. 
• Checksum: Not Required 
• Standard Used by the Code: ANSI/AIM BC3 
• Variations of the Code: Codabar 

 7. GS1 DATABAR 

 GS1 DataBar is a compact barcode designed for retail applications that require more product-related information than standard UPC or EAN codes. Examples of this type of barcode include vegetables or fruits, gift certificates, or items sold by weight. 

Unlike traditional retail barcodes, the GS1 DataBar can encode product attributes, such as weight, selling price, and expiration status. Therefore, with the use of GS1 DataBars, retailers will be better able to maintain accurate inventory and improve checkout procedures.  

GS1 DATABAR: Overview of Characteristics 

• Data Capability: Numeric includes Application Identifiers (AI). 
• Industries Utilized by the Code: Retail and Grocery. 
• Checksum: Required 
• Standard Used by the Code: GS1 General Specifications 
• Variations of the Code: Omnidirectional, Expanded, Truncated. 

8. MSI PLESSEY 

The MSI Plessey is a numeric barcode designed primarily for warehouse inventory control and retail back-office systems. It is easy to implement and is a good choice in a closed-loop environment where global standards are not necessary. 

• MSI PLESSEY: Overview of Characteristics 
• Data Capability: Numeric Only 
• Industries Utilized by the Code: Warehouse and Inventory Management 
• Checksum: Not Required 
• Standard Used by the Code: MSI Specification 
• Variations of the Code: MSI-10 and MSI-11 

Types of Barcodes: 2-D Barcodes  

 1. QR Codes 

A QR code is a 2-D barcode that can store large amounts of information in a compact square. QR codes are used in many ways across marketing, payments, authentication, and consumer interaction because of their compatibility with smartphone cameras.  

 The QR Code’s data-encoding capabilities enable businesses to encode URLs, text, identifiers, and structured data, making them ideal for both consumer and business applications.   

Support for Data:  

• Data Types: Numeric, Alpha, Binary 
•  Industry use: Marketing, Payments, Retail, Manufacturing 
•  Error Correction: Built-in error correction with 4 levels 
•  Standard: ISO/IEC Standard 18004 
•  Barcode Variation: Model 1, Model 2, Micro QR  

2. Data Matrix Code  

Data Matrix code design can hold a high density of data in a small amount of space ideal for the manufacturing, electronics, and healthcare industries for Direct Part Marking (DPM).  

Data Matrix codes facilitate accurate decoding even when they sustain partial damage, making them well-suited for the harsh working conditions common in the manufacturing industry. 

Support for Data:  

• Data Types: Numeric, Alpha, Binary 
• Industry use: Manufacturing, Healthcare, Electronics 
• Error Correction: ECC 200 
• Standard: ISO/IEC Standard 16022 
• Barcode Variation: ECC 200  

3. PDF417 Code  

PDF417 is both a stacked linear barcode and is capable of holding large volumes of information. The PDF417 code is popular on shipping materials, government-issued identification cards, and transportation documents. 

PDF417 can hold a large amount of data and is often used to add detailed information directly to the barcode. 

 Features of PDF417 at a glance: 

• Data Capabilities – Text + Binary + Images  
• Industries – Logistics, Government, Transportation  
• Built-In Error Correction – Reed-Solomon  
• Standards – ISO/IEC 15438  
• Variations –Standard PDF417 + MicroPDF417  

4. Aztec Code: 

The Aztec Code is a compact 2D barcode that helps with easy decoding without a quiet zone. Its primary use is for ticketing and mobile boarding passes. 

 Its compact design makes it suitable for digital displays, and its durability ensures it remains effective even on low-quality printed materials. 

Features of Aztec Code at a glance: 

• Data Capabilities – Numeric + Alphanumeric + Binary  
• Industries – Transportation + Ticketing  
• Built-in error correction  
• Standards – ISO/IEC 24778  
• Variations – Aztec + Compact Aztec  

Choosing the Right Barcode for Your Needs.  

Choosing the right barcode for your needs depends on many factors. There is no standardized “one-size-fits-all” barcode that you can use for every application. 

Some of the factors to consider when making your decision are;  

1. How much data do you want your barcode to store? 
2. Where will your barcode be printed? 
3. Where do I plan to scan my barcode? 

Are there any regulatory or industry requirements related to the use of barcodes? Important considerations when selecting a barcode include:

• Amount of Data – Number or numeric + alphabetic + metadata  
• Scanning Environment – Type of light, scanning distance, and speed  
• Label Size and Surface Area – Small parts or large packages  
• Durability – Damage, abrasion, or distortion of the barcode  
• Compatible With Devices – Hand-held scanner, camera, and/or mobile device  

Compliance With Regulations Or Industry Standards – GS1, Industry mandates, or local, state, or federal regulations. 

Industry-Specific Barcode Types 

The types of barcodes used in different industries can vary. Here are the most common barcodes by industry: 

• Retail (UPC, EAN, GS1 DataBar) 
• Logistics/Warehousing (Code 128, ITF, PDF417) 
• Manufacturing/Automotive (Data Matrix, Code 39) 
• Healthcare (Data Matrix, QR) 
• Marketing/Payments (QR codes) 
• Transportation (Aztec, PDF417) 

Implementing Barcodes – Step-by-Step Guide 

Step 1: Identify Your Use Case 

Determine the ideal barcode to track: products, parts, assets, documents, or shipments. 

Step 2: What Type of Barcode Should You Use? 

Once you identify the number of items to label, you can determine whether to use a 1D or 2D barcode based on the data volume per item, the available label space, and the scanning location. 

Step 3: What Are the Printing and Labeling Conditions? 

Choose printing and labeling methods with the appropriate quality and durability for real-world usage conditions. 

Step 4: How Will You Verify the Scanner’s Compatibility with Your Barcode? 

Test the barcode against every type of scanner to ensure it scans barcodes in your organization. 

Step 5: How Will You Test, Monitor, and Scale the Barcode System? 

Run a pilot program to test the accuracy of scans and revise the process before moving forward with the full implementation of the barcode system. 

In Summary  

Barcodes may seem like a simple tool, but when you need to choose the right type of barcode for your organization, it can have a HUGE impact on your operational efficiency, compliance, scalability, and data-capture accuracy. 

Understanding the various barcode types, their strengths/weaknesses, and how they work will help you create a barcode strategy that enables you to grow your organization rather than create a bottleneck. 

Barcodes don’t just store data; if used correctly, they can provide your entire organization with improved visibility into its operations, faster inventory movement, and greater confidence in the integrity of your inventory. 

In today’s world, drones have progressed as a technological headway that helps businesses to utilize them in building capabilities. The advancement in artificial technology and computer vision has created new ways of adopting drone technology in the business process. The applications of drone scanning vary and cover a wide range of industries such as agriculture, field services, and inventory management.

However, there is still a significant opportunity for other industries and technology companies to implement drone scanning to save costs and improve efficient workflows in their enterprise.

Drones as a scanning device

Industries are already shifting toward software-based scanners for efficient automation of their workflow process. Unlike conventional scanners, software scanners can be integrated into any existing smart device such as smartphones, tablets, wearables, drones, etc. They have the capability of transforming any smart device into an enterprise-grade scanner.

In certain scenarios of scanning, workers need to scan items placed on high shelves which require some external support such as a forklift or reach truck with which the workers have to manually scan the stocks and inventory. This procedure is time-consuming, expensive, and dangerous to workers handling it. Drone scanning is a game-changing solution for scanning products and pallets in warehouses. The drone captures data from text, barcodes, or any other object and provides the workers with real-time insights. Drone scanning has the ability to capture high-quality videos from aerial angles at any orientation.

Some of the use cases of drone scanning are farm surveying, tower inspections, and stock counting in large warehouses. These applications are gaining popularity, and help businesses to save money, improve safety, and produce quality results. Drone scanning requires less labor, reduces workforce hazards, and saves time and money

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Drones help scan from places where workers cannot access them and provide instant information on the product or items being scanned without any errors. Drone scanning will act as an excellent method of liberating workers from potentially hazardous situations and the scanning can be completed without any human support from a safe distance.