Top AI License Plate Recognition Systems for Parking Facilities

The first time I watched a parking garage lose nearly two hours of vehicle data because of a fogged-up camera lens, the security manager just stared at the monitor and said, “We spent six figures for this?” That stuck with me. A lot of parking operators jump into AI license plate recognition systems expecting instant accuracy, automatic access control, and fewer staffing headaches. Then reality shows up in the form of glare, dirty plates, tailgating vehicles, and software that looked great in a sales demo but folds under real traffic pressure.

According to the International Parking & Mobility Institute, parking facilities using automated vehicle access systems can reduce entry wait times by up to 40%. That’s not a small quality-of-life upgrade. In busy commercial garages, shaving even five seconds off every vehicle entry adds up fast. And yeah, that matters more than you’d think when tenants start complaining about morning backups.

What surprised me over the years wasn’t the AI itself. It was how wildly different these systems perform once weather, lighting, and real drivers enter the picture. One garage in Dallas had a premium setup with expensive vehicle recognition cameras, but half the plates failed after sunset because the installer mounted the cameras too high. Meanwhile, a smaller mixed-use property nearby used a simpler OpenALPR deployment with smarter positioning and got better accuracy for less money. Sound familiar?

Why Parking Operators Are Replacing Manual Gate Logs With AI License Plate Recognition Systems

Manual access logs are kind of like using a flip phone in a world full of smartphones. Technically? They still work. But they slow everything down, create avoidable mistakes, and depend way too much on human consistency.

Modern AI license plate recognition systems automatically capture, verify, and log vehicles entering or exiting a facility in real time. That means fewer gate attendants manually checking credentials and fewer angry drivers stuck behind a stalled barrier arm.

Here’s the thing…

Parking operators aren’t just buying convenience anymore. They’re buying accountability. If a vehicle damages property, overstays, or enters a restricted area, the footage and plate records become instantly searchable instead of buried in hours of video.

Facilities using AI video analytics and monitoring tools are also tying plate recognition into broader security ecosystems. That includes suspicious activity detection, occupancy monitoring, and automated visitor management. Think of it like upgrading from a flashlight to a full lighting system. Same goal. Totally different visibility.

A few practical wins operators usually notice first:

• Faster tenant and employee entry
• Reduced tailgating incidents
• Better overnight security logging
• Less dependency on physical access cards

No, seriously. Access cards disappear constantly. Plates don’t.

Recognition Accuracy in Rain, Glare, and Night Conditions

This is where weaker LPR security software gets exposed fast.

A lot of vendors advertise “99% recognition accuracy,” but they rarely explain the testing environment. Bright daylight. Clean plates. Slow-moving vehicles. Perfect conditions. Real parking lots? Completely different story.

Good parking surveillance AI needs to handle:

• Headlight bloom at night
• Heavy rain distortion
• Sun glare at gate angles
• Dirty or bent license plates

Systems from companies like Genetec and Vaxtor tend to perform better because they train their models using varied environmental conditions instead of controlled lab footage. According to a 2024 report from MarketsandMarkets, weather-resistant AI imaging has become one of the fastest-growing segments in automated parking infrastructure.

Honestly? This part surprised even me. Some mid-range cameras outperform premium models simply because their infrared tuning is spot on for low-speed gate traffic.

Cloud vs On-Premise Vehicle Recognition Cameras

Okay, so this debate gets heated fast in commercial security circles.

Cloud-based AI license plate recognition systems are easier to manage remotely and usually update faster. They’re a solid pick for operators managing multiple parking sites from one dashboard.

On-premise systems, though, still dominate facilities with strict privacy requirements or unstable internet infrastructure. Hospitals, government buildings, and enterprise campuses often prefer local processing because they control every piece of stored vehicle data directly.

Here’s a quick breakdown:

Feature	Cloud-Based LPR	On-Premise LPR
Remote Management	Excellent	Limited
Upfront Hardware Cost	Lower	Higher
Internet Dependency	High	Low
Data Control	Shared Environment	Fully Local
Software Updates	Automatic	Manual

If you ask me, cloud systems make more sense for most mid-sized parking operations. But if your facility handles sensitive tenant traffic, local processing is still the safer move.

And while people obsess over software dashboards, the actual smart CCTV systems with AI motion detection underneath often matter more than the interface itself.

How Fast License Plate Matching Really Needs to Be

Spoiler: faster isn’t always better.

A lot of operators chase ultra-low latency because vendors market millisecond processing like it’s a Formula 1 race. But parking facilities aren’t high-speed toll roads. Vehicles are already slowing down at gates.

What matters more is consistency.

A stable 1-second plate verification with high confidence beats a shaky 0.2-second read that triggers false denials. Been there? Drivers absolutely lose patience when gates hesitate or reject authorized vehicles during peak hours.

Think of AI recognition like restaurant timing. If the kitchen rushes every order but gets half of them wrong, customers still leave annoyed.

That’s why the better LPR security software platforms prioritize:

• Confidence scoring
• Duplicate filtering
• Plate history matching
• Lane-specific tracking

The best systems quietly avoid problems before operators even notice them.

What Actually Makes a Good LPR Security Software Platform?

A strong AI license plate recognition system does more than identify numbers on a plate. Real talk: the camera is only half the equation. The software determines whether your operation runs smoothly or turns into a daily troubleshooting session.

The usual suspects all advertise AI analytics, automated alerts, and smart vehicle matching. Fair enough. But the platforms worth paying for tend to share a few practical traits that don’t show up prominently in marketing pages.

First, they handle bad footage gracefully. Dust, compression artifacts, partial obstructions — good systems recover better instead of instantly failing recognition.

Second, they integrate cleanly with existing infrastructure. A lot of parking operators already use best cloud video surveillance platforms tied into gate arms, ticketing systems, and tenant databases. Replacing everything at once is not exactly cheap, and more often than not, unnecessary.

Third, reporting matters way more than people expect.

One facility manager I worked with originally focused only on gate automation. Six months later, he cared far more about traffic trend reports because they helped renegotiate tenant parking allocations. That’s where modern parking surveillance AI starts becoming operational intelligence instead of just security tech.

Here’s what most people miss: plate recognition accuracy means almost nothing if your database management is messy. Duplicate vehicle entries, expired tenant lists, and inconsistent permissions create more headaches than camera failures nine times out of ten.

Platforms tied into broader AI monitoring systems also tend to scale better because they’re built for event-based automation rather than isolated gate control.

The Hidden Cost of Cheap Parking Surveillance AI Setups

Low-budget AI license plate recognition systems usually fail in predictable ways.

Not immediately. That’s the trap.

The demo works. The first month feels fine. Then seasonal weather changes hit, nighttime glare increases, or traffic volume doubles during holiday periods. Suddenly operators are manually reviewing plates again because false reads spike.

I’ve seen facilities spend less upfront only to replace entire camera arrays within eighteen months. At that point, the “cheap” option becomes the expensive one.

Quick heads-up: most failures don’t come from the AI engine itself.

They come from:

• Weak infrared illumination
• Improper mounting height
• Generic consumer-grade cameras
• Underpowered local servers

One retail complex tried using standard security cameras instead of dedicated vehicle recognition cameras to save money. The result? About 30% of overnight entries failed recognition because motion blur ruined the footage.

That’s why specialized AI surveillance cameras that detect suspicious activity usually outperform adapted consumer systems. They’re designed around vehicle motion behavior instead of general observation.

Why Bad Camera Placement Ruins Even Expensive Systems

Look, I get it. Everyone wants to blame the software first.

But camera placement is low-key one of the biggest factors in LPR performance. A $3,000 camera mounted poorly can perform worse than a modest setup installed correctly.

The sweet spot usually includes:

1. Mounting height between 3 to 5 feet above plate level
2. Horizontal approach angles under 30 degrees
3. Dedicated lane framing
4. Controlled lighting near entry points

That second point matters a lot. Once angles become too aggressive, plates distort like a warped mirror reflection.

For operators exploring broader video analytics tools for retail and commercial spaces, plate recognition works best when designed as part of the traffic flow itself — not added as an afterthought.

And yeah, that changes everything.

The interesting part is what happens after operators realize the software alone isn’t the magic fix. That’s usually when they start asking smarter questions about scaling, long-term maintenance, and whether the “premium” systems are actually worth the money.

Best AI License Plate Recognition Systems for Small Parking Lots

Smaller parking facilities don’t need airport-level infrastructure. Real talk: a 40-space medical office garage has very different needs than a downtown mixed-use tower with six entry lanes and monthly tenant subscriptions.

That’s why choosing AI license plate recognition systems based on actual traffic patterns matters way more than chasing the flashiest feature list.

Here’s where smaller facilities usually get it right:

• Simpler lane layouts
• Lower hardware costs
• Faster deployment timelines
• Easier staff training

The mistake? Buying enterprise systems they’ll never fully use.

Vaxtor for Smaller Commercial Properties

Vaxtor has quietly become a solid option for operators who want reliable LPR security software without drowning in enterprise complexity.

Its biggest strength is flexibility. You can run it through existing VMS platforms instead of replacing your entire security ecosystem. For facilities already using AI warehouse surveillance tools, that compatibility becomes an easy win.

What I like most is the traffic adaptability. Vaxtor handles moderate-speed vehicle entry well without requiring ridiculously expensive camera hardware.

Not gonna lie — setup still matters a lot. But once dialed in, the system performs consistently for:

• Apartment complexes
• Office garages
• Retail parking structures
• Gated communities

And unlike some vendors, the licensing costs don’t spiral completely out of control once you add extra lanes.

OpenALPR for Budget-Conscious Operators

OpenALPR sits in an interesting middle ground.

It’s not the prettiest platform. The interface feels a little dated compared to newer cloud dashboards. But for operators willing to spend time fine-tuning configurations, it’s kind of a big deal for the price.

One municipal garage I visited used OpenALPR paired with carefully positioned vehicle recognition cameras and ended up outperforming a neighboring private garage running a far pricier setup. Why? Better installation discipline.

Here’s the thing…

Cheap software becomes dangerous when operators expect it to self-correct bad infrastructure. OpenALPR works best when paired with:

• Dedicated LPR cameras
• Stable nighttime lighting
• Controlled gate speeds
• Clear lane separation

That combination is often “good enough” for facilities under 300 daily vehicle entries.

If your operation also handles visual asset tracking or commercial media workflows, platforms connected with digital asset management for brands principles can actually improve long-term footage organization too. Weird crossover, but legit useful.

PlateSmart for Multi-Entry Sites

PlateSmart tends to shine in facilities with complicated access flows.

Think hospitals. University campuses. Multi-building business parks.

These environments create constant edge cases — temporary vehicles, delivery drivers, rotating staff permits, emergency access overrides. Simpler parking surveillance AI systems often struggle once exceptions pile up.

PlateSmart handles layered permissions surprisingly well.

Its analytics dashboard also gives operators more visibility into:

Feature	Vaxtor	OpenALPR	PlateSmart
Best For	Small Commercial Lots	Budget Deployments	Multi-Entry Facilities
Ease of Setup	Medium	Medium-Hard	Medium
Scalability	Good	Moderate	Excellent
Reporting Tools	Strong	Basic	Advanced
Cloud Support	Yes	Partial	Yes

If you ask me, PlateSmart is worth the higher price if your traffic patterns constantly change throughout the day. Otherwise, smaller facilities may never fully use the advanced logic tools.

Enterprise AI License Plate Recognition Systems Worth Paying For

Once traffic volume climbs above a few thousand daily entries, weaker systems start showing cracks fast.

This is where enterprise-grade AI license plate recognition systems separate themselves from entry-level tools. Not because they magically read plates better, but because they survive operational chaos better.

And trust me, parking chaos gets messy.

One convention center deployment I reviewed handled over 11,000 vehicle entries during a three-day event cycle. Temporary permits. Ride-share surges. Vendor trucks. Rainstorms. The whole vibe. A lightweight system would’ve folded instantly.

Genetec AutoVu for Large Facilities

Genetec AutoVu is hands down one of the strongest enterprise LPR security software platforms available right now.

The big advantage? Ecosystem integration.

Operators can tie AutoVu into:

• Security operations centers
• Tenant databases
• Access control systems
• Incident management tools

Facilities already using AI facial recognition software for access control often integrate smoothly because Genetec’s architecture is built around layered authentication.

Here’s where it gets interesting.

AutoVu’s contextual analytics matter more than raw plate reads. The platform tracks vehicle behavior patterns over time instead of treating every detection as isolated data.

That means operators can flag:

• Repeated unauthorized entries
• Suspicious circulation patterns
• Stolen vehicle matches
• Occupancy anomalies

According to Security Industry Association reporting from 2024, integrated analytics systems reduced manual parking enforcement workloads by nearly 35% in large commercial deployments.

That’s real operational savings. Not just marketing fluff.

Axis Communications With Deep Learning Analytics

Axis doesn’t always get the same flashy attention as pure-play AI vendors, but their hardware reliability is low-key one of the best in the industry.

Especially for outdoor environments.

Heavy humidity, temperature swings, dust buildup — Axis vehicle recognition cameras tend to stay stable longer with less maintenance intervention.

That matters because downtime kills confidence fast. A parking operator can forgive one bad recognition event. Weeks of recurring maintenance? Different story.

Facilities combining AI crowd monitoring systems with vehicle analytics also benefit from Axis edge processing because bandwidth loads stay manageable even during heavy traffic events.

Think of edge AI like having a sous chef prep ingredients before they reach the main kitchen. Less strain. Faster execution. Fewer bottlenecks.

Hikvision Vehicle Recognition Cameras for High Traffic Volume

Okay, so Hikvision always sparks debate.

Some operators love the price-to-performance ratio. Others raise concerns around compliance and procurement restrictions depending on region or industry.

Fair warning: the answer might surprise you. Technically speaking, Hikvision’s vehicle recognition cameras are often extremely capable for dense traffic scenarios. Especially in controlled-access commercial garages.

The challenge isn’t always performance.

It’s long-term governance.

Facilities subject to stricter procurement standards or compliance frameworks should carefully evaluate deployment rules before committing. That’s why many operators also review AI video monitoring compliance laws before expanding plate recognition systems.

Still, from a pure operational standpoint, Hikvision handles:

• High frame-rate capture
• Multi-lane monitoring
• Nighttime infrared balancing
• Fast-moving vehicle identification

surprisingly well for the cost.

Parking Surveillance AI vs Traditional Access Control: Which One Wins?

Short answer: AI license plate recognition systems usually win for convenience. Traditional access control still wins for layered verification.

The smartest facilities use both together.

Here’s why.

Traditional systems rely on cards, fobs, PIN codes, or human verification. Problem is, humans forget things. Cards get shared. PINs get leaked. Gate attendants make mistakes.

Parking surveillance AI removes friction because the credential becomes the vehicle itself.

That sounds simple. But operationally? Huge difference.

System Type	Biggest Strength	Biggest Weakness
AI License Plate Recognition Systems	Fast vehicle entry	Camera dependency
Access Cards/Fobs	Familiar workflow	Lost or shared credentials
PIN-Based Entry	Low setup cost	Security vulnerabilities
Human Gate Verification	Flexible decision-making	Slow and inconsistent

No brainer for most commercial operators: combine LPR with secondary verification for sensitive zones.

One logistics property I visited used AI plate recognition for perimeter entry, then required tenant QR validation for loading dock access. Smart setup. Reduced congestion without weakening security.

And yeah, facilities already investing in broader security software ecosystems usually adapt faster because their teams already understand event-based monitoring workflows.

How to Choose the Right Vehicle Recognition Cameras for Your Facility

Most buying mistakes happen before installation even begins.

Operators focus on megapixels instead of traffic conditions. That’s like buying hiking boots based only on color. Looks nice. Won’t help much once conditions get rough.

Here’s a simple framework I recommend:

1. Measure average vehicle speed at entry points
2. Identify nighttime lighting conditions
3. Calculate peak hourly traffic volume
4. Determine cloud vs local processing needs
5. Test one lane before scaling facility-wide
6. Review future integration requirements

That fifth step? Totally worth it.

Pilot deployments expose blind spots before operators commit to massive hardware purchases. One facility caught severe reflective glare issues during a two-week test run and avoided replacing 18 cameras later.

Systems tied into broader AI monitoring and smart surveillance workflows also tend to future-proof better because operators can expand analytics capabilities over time instead of rebuilding from scratch.

Indoor Garage vs Outdoor Lot Requirements

Indoor garages are generally easier environments for AI license plate recognition systems.

Lighting stays controlled. Weather stays predictable. Vehicle speeds remain lower.

Outdoor lots? Different story completely.

Rain scatter, direct sunlight, snow buildup, dust, insects — outdoor vehicle recognition cameras fight environmental interference constantly.

That’s why operators managing mixed environments should prioritize:

• Weather-rated camera housings
• Dedicated infrared illumination
• Regular lens maintenance schedules
• Stable network infrastructure

No, seriously. Dirty lenses alone wreck recognition quality more often than most people realize.

Outdoor deployments also expose something most brochures conveniently ignore: AI license plate recognition systems are only as reliable as the habits behind them. If nobody cleans lenses, checks alignment, or reviews false-read reports, performance slowly drifts until operators stop trusting the system entirely.

That loss of trust is expensive.

The Best Camera Angles Most Installers Still Ignore

A surprising number of installers still prioritize “coverage” over plate readability. Sounds logical at first. More visible area should mean better security, right?

Not exactly.

Wide-angle positioning often creates stretched or distorted plates once vehicles approach at sharper angles. The AI can technically see the vehicle, but plate parsing accuracy drops fast.

The sweet spot usually looks boring. That’s the funny part.

The most reliable vehicle recognition cameras often focus tightly on:

• One dedicated lane
• Controlled vehicle speed
• Predictable approach angles
• Stable lighting zones

That narrower framing creates cleaner reads with fewer processing errors.

Think of it like trying to read a street sign while sprinting sideways versus standing directly in front of it. Same sign. Totally different readability.

One shopping center garage in Phoenix improved recognition rates from 81% to 96% simply by lowering camera height and reducing lane width visibility. No software changes. No new servers. Just smarter positioning.

Facilities already investing in best AI security monitoring software for offices often see similar gains because modern monitoring platforms make camera calibration easier to track over time.

What Nobody Tells You About AI License Plate Recognition Systems and Compliance

Here’s where things get uncomfortable for some operators.

Collecting vehicle data creates responsibility whether teams are ready for it or not.

A lot of parking facilities install parking surveillance AI without fully thinking through retention policies, tenant disclosure requirements, or local privacy expectations. Then six months later, legal teams show up asking questions nobody prepared for.

Real talk: compliance planning should happen before installation, not after.

According to the International Association of Privacy Professionals, automated surveillance systems storing identifiable vehicle information increasingly fall under broader data governance scrutiny in many regions.

That doesn’t mean operators should panic. It just means policies matter.

Strong LPR security software platforms usually include:

• Configurable retention windows
• Access permission controls
• Audit logs
• Automated deletion schedules

The systems that skip those controls? Totally skippable for serious commercial operations.

Data Retention Rules That Catch Operators Off Guard

Most facilities keep plate data way longer than necessary.

Honestly, it depends — but here’s how to tell if your retention window makes sense: if nobody can explain why the data still exists after several months, it probably shouldn’t.

Common retention periods look like this:

Facility Type	Typical Retention Window
Residential Parking	30–60 Days
Commercial Office Garages	60–90 Days
Airports & Transit Hubs	90–180 Days
Government Facilities	Case-Specific

The challenge is balancing operational usefulness with unnecessary exposure.

And yeah, this matters more than operators think. Long-term data storage increases liability during disputes, audits, or security incidents.

Parking operators exploring broader AI content categorization software principles often adapt faster because they already understand structured data governance workflows.

Privacy Expectations From Customers and Tenants

People notice surveillance more now. Especially younger tenants.

Not always in a paranoid way. More like: “Who has access to this data and how long are you keeping it?”

Fair enough question.

One corporate garage manager told me employees became more comfortable after the facility clearly explained its retention policies and restricted access controls. Transparency helped reduce complaints almost overnight.

That’s why visible disclosure signage matters.

And if operators want a better understanding of the broader history behind automated recognition technologies, the Wikipedia article on automatic number-plate recognition gives useful context without getting buried in technical jargon.

Real Parking Facilities Using LPR Security Software Successfully

The best deployments usually aren’t the flashiest.

One mixed-use residential tower in Atlanta used AI license plate recognition systems primarily to stop unauthorized overnight parking. Simple goal. Nothing fancy.

Within three months:

• Tenant complaints dropped significantly
• Security staff reduced manual patrol time
• Visitor verification sped up noticeably
• Repeat violators became easier to track

Another example came from a hospital parking structure that integrated cloud-based surveillance platforms with plate analytics to improve emergency vehicle access. Instead of waiting for gate personnel, ambulances entered automatically based on registered fleet databases.

That’s the kind of practical deployment operators actually care about.

Not flashy dashboards. Fewer bottlenecks.

One thing I’ve consistently noticed? Facilities that succeed with parking surveillance AI usually treat it as an operational tool first and a “security gadget” second.

Big difference.

Common Buying Mistakes Parking Managers Make

Most mistakes happen because operators buy based on fear instead of workflow realities.

They worry about theft, unauthorized access, or congestion — all valid concerns — but then purchase oversized systems loaded with features nobody on-site will realistically manage.

Here’s what tends to backfire most:

• Buying cameras before evaluating lane geometry
• Ignoring nighttime lighting conditions
• Underestimating maintenance requirements
• Choosing systems with weak integration support

What nobody tells you is that support quality often matters more than feature count.

A mid-tier AI license plate recognition system with responsive technical support will usually outperform a fancy platform nobody understands how to maintain.

And if your property already uses AI video analytics software for retail and commercial monitoring, compatibility testing should happen early. Otherwise teams end up juggling disconnected dashboards that create more confusion than efficiency.

Frequently Asked Questions

How accurate are AI license plate recognition systems in bad weather?

Great question — and honestly, most people get this wrong. Accuracy depends less on the AI itself and more on camera placement, infrared lighting, and vehicle speed control. High-quality systems can still maintain 90%+ recognition rates during rain or nighttime conditions if the setup is calibrated properly. Cheap hardware with poor lighting? That’s usually where failure rates spike.

Can small parking garages afford LPR security software?

Yes, especially now that cloud-based options have lowered upfront infrastructure costs. Smaller facilities can often start with a single-lane deployment and scale later instead of building everything at once. For many operators, spending between $3,000 and $15,000 upfront is enough for a functional entry-level system depending on camera count and software licensing. The key is avoiding oversized enterprise packages you’ll never fully use.

Do vehicle recognition cameras work without internet access?

Short answer: yes. But here’s the nuance — many systems can process plates locally using edge devices or on-premise servers. However, cloud dashboards, remote reporting, and software updates may become limited without stable connectivity. Facilities in areas with unreliable internet often combine local processing with periodic cloud syncing.

How long should parking facilities store plate data?

Honestly, it depends — but here’s how to tell: keep data only as long as there’s a clear operational reason. Most commercial garages land somewhere between 30 and 90 days. Longer retention periods can increase legal exposure if operators don’t have proper access controls or deletion policies in place. Simple, documented retention rules usually work best.

Are AI license plate recognition systems better than access cards?

For vehicle entry convenience? Absolutely. Drivers don’t need to stop, scan, or remember credentials. That said, high-security facilities still pair plate recognition with secondary verification methods like QR codes or tenant databases. Nine times out of ten, layered security works better than relying on one system alone.

What camera resolution works best for parking surveillance AI?

Most commercial deployments perform well with 1080p or 4MP dedicated LPR cameras. Higher resolution sounds impressive, but it can increase storage and processing loads without dramatically improving plate reads if positioning is wrong. Controlled lighting and correct mounting angles usually matter more than chasing massive megapixel numbers.

Can AI license plate recognition systems integrate with existing surveillance setups?

Okay so this one depends on a few things. Platforms like Genetec, Vaxtor, and Axis generally integrate well with broader security ecosystems, especially if the facility already uses modern VMS infrastructure. Older analog systems may require additional encoders or hardware upgrades. Testing one lane first is usually the smartest move before expanding site-wide.

Your Move

If you’re evaluating AI license plate recognition systems right now, don’t get distracted by flashy dashboards or inflated accuracy claims. Start with your traffic flow. Seriously. Watch how vehicles actually move through the property during peak hours and overnight conditions before buying anything.

That single step reveals more than most vendor demos ever will.

The strongest parking surveillance AI deployments usually come from operators who keep things practical: controlled lanes, stable lighting, manageable workflows, and software their teams can realistically maintain long term.

And yeah, the tech matters. But discipline matters more.

Facilities already exploring broader AI monitoring and surveillance technologies often adapt faster because they think operationally instead of emotionally. That mindset shift is low-key one of the biggest advantages you can have going into a deployment.

One last thing. Don’t treat vehicle recognition cameras like a “set it and forget it” appliance. They need periodic testing, cleaning, and review — kind of like maintaining good tires on a truck fleet. Ignore the basics long enough, and eventually performance slips whether you notice immediately or not.

If you’ve already deployed an LPR system or you’re weighing different options right now, share your experience or biggest challenge in the comments. Somebody else is probably dealing with the exact same thing.