LPR/ANPR Guide

By Sean Patton, Published Mar 25, 2021, 01:10pm EDT

This 16-page guide explains the fundamentals of license plate recognize / automatic number plate recognition.

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Inside we cover:

  • Traditional OCR LPR
  • OCR Visually Equivalent Characters
  • Deep Learning LPR
  • Plate Datasets
  • Accuracy Claim Issues
  • Machine Learning Detection
  • Detection More Difficult Than Number Recognition
  • Detection Challenges Significant
  • Vehicle Speed
  • Angle Of Plates
  • Damaged Plates
  • Weather
  • Lighting
  • License Plate Variables
  • Mobile and Fixed
  • Dedicated LPR
  • Software Only LPR

This part of our Video Analytics Course starting at the end of March.

LPR/ANPR ********

******* ***** *********** (***) or ********* ******-***** *********** (ANPR) ********** **** **** used *** **** ***** to ****** ******* ******* plates *** ****** *** alphanumeric ********** ** *** plate. *** ** ******** marketed *** *** ***********, parking, *** ****** ********. Compared ** **** ***** video *********, *** ********** is **** ******.

***** *** **** ******* Character *********** (***) *** decades, **** ********-***** ********** have *****. *****, * hybrid ******** ** ******* and **** ******** **** OCR ** ******.

Two-Step *******

***/**** ******** ** *** fundamental *****:

  • **** ***** *** ***** is: ***** ************ ****** can ******* **** **********, ranging **** **********, ***********, company ***** ** ********, etc. *** ****** ***** needs ** ***** **** characters ** ***** ** the ******* / ****** plates ****.
  • **** *** ********** ** the *****: **** *** plate ** *****, *** characters **** ** ** read, ****** **** ** complicated ** ******* ******* including *****, *****, ********** images, *****/*****, ***.

Traditional *** ***

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*** (******* ********* ***********) takes * ****** ********* as ** *****, *** either ******* ** ******* complete ********** ** ***** the ********* **** ****** and ******* ***** ****** against ******** *** **********.

*** ******* '*' ** created **** *** ****** line **** **** ** right, *** ****** **** from ***** ** ****, and * ********** **** in *** ******. ** finding *** ***** ** the *********, *** ********** it ** ** '*'.

***** ******** ** **** resilient ** ******* ** font *** ****. *******, OCR ********* **** *** work **** ***** ****** of **** ** ********** characters.

*** ***** **** ** uniform-sized, ****** ****** **********, and ** ******* **** are ******** ** ** differentiable.

*** *******, ***** ** plates **** * ****-******** monochrome **********, ***** ***** letters, ***** ******* **********, and ********* ******** ********** with ******* ** ****** differentiate ******* **********:

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******* ********* ****** *** particularly *********** ** *** because **** ********** *** designed ** **** **** left ** *****, *** the ***** ** * stacked ********** *** *** going ** **** **** any ***** ********* *******:

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OCR ******** ********** **********

* ******* ******** ** OCR ********** ** **** characters ** ****** ******* plate ***** **** **** are ******, ** *** domain, ****** *********** (*.*. O ** * ** 0 (****), * ** 5, * ** *, B ** *).

**** ***** **** * license ***** **** *** characters "*******" ***** ***** read ** "*******", ** "ABCI234":

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**** ************* ********* *** challenge ** ************* *********** a ******* ***** ** a ***** ****. *********** ******* ********** **** ** determine ** ****** **** are **********, ****** **** were *******, *** **********, or *** ******** **** be ****** **********.

** ******* ****, **** LPR ********** **** *********** adjustment ********** ** ******** the **** ** ****** license ******, **** ** commonly ******** ** ** "fuzzy" ********. *******, **** increases *** ***** ******** rate, ***** ** * compliance ***** *** ******** and *******, ******* ******** with ********** ******* ****** will ** ******* ****** to ***** **** ****** not **** ******.

Deep ******** ***

**** ****** *** ********* use **** ******** ********** to **** ******. ***** some *** ***** **** learning, **** ********** ********* use * *********** ** machine ********, **** ********, and ***:

  • ***** **** ******** (******* *********, **** *** Read ******): ****** ********, ****** plates, *** **** **********.
  • **** ******** *** ****** (** ******* *********, **** and **** ******): ****** ******, *** read **********.
  • **** ******** ********* (**** ****** ****): ** ******* *********, detect ****** **** *** OCR ** ****

***** **** ******** ******* use ******* ************** ************ *** ***, which **** * *****-**** process ** **** ******. Partially **** ******** ********** use **** ******** ** find ****** *** ****** the ***** *** ***** and ********, **** *** to **** *** **********:

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*******, ** *** * higher ************* **** **** OCR. **** ** ********** important ** *** ******* high *** (** ***+) is ****** ** **** plates **** ****-****** ****, unlike******* ******/****/******* ************** sufficient.

**** ******** ** **** accurate **** *** *** more ********* ** *********. It ***** ****** **** smudges, ***** *********, ******** changes, *** ********* ******.

************, ******* ********** *** less ** ** ***** than **** *** ******* deep ******** ********** *** looking *** *******, *** just ******* *** *****:

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**** ******** *** ** also ***** ******* **** other ********* **** ******* classification (*****, **** *****) and ******* ******* ********** without ********** *******.

Plate ********

**** ******** *** ** trained ************ ***** ********, ******* ** ***** deep ******** ********** (*.*.****** ***********), *** ** ********* accessible *** ******* *** development.

*** ******** ** *, and ** **** ***** of *** *****, ******* plates *** ********* *******, meaning ******** ******* ******* updating *** ********* ** retrain ***** **********.

Accuracy ***** ******

**** *** ************* **** ************ **** True ******** **** *** accuracy (******* ********* ******** *****), ***** ** *********** because ** ******* *** license ****** **** ** did *** ******.

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*** *******,****** ** "**%" ******** recognizing *** ************ ********** on * ******* ***** are ******, *** **** generally ***** **** ****** that **** **** *********** count ******* **** ******** metric, *** ****** **** were *** **** ** all, ***** ** ****** in *** ****-*****.

****** ****** **** ***** missed ****** (******* *****, high ******, *** *******, damaged ******, ******** ******) are *** ******* ******* the ********, ***** **** not ******* *** ****-***** performance ** *** ******** system.

Machine ******** *********

***-***** *** **** ******* learning ** **** ******, using ******* *********** ** find *** ******** *** edges ** *** ******* plate:

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**** *********** *** **** good ** ********* ***** and ***** ** ******* using ********* ******* ***********, just ********** *********** (**** *****). **** *********** *** sliding ******* *** ******** feature *******, ** **** plates ** ********:

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**** ******* ******** ********* algorithms **** ***-********** ****** for ******** ******* ***** height *** ***** (*.*. US ******* ****** **** approximately * *:* ***** to ****** *****).*******, ******* ** ****, machine ******** *** ** also **** ** ******* plate-shaped ****, **** ** not * ******* *****:

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******* ******** ** *************** easier **** **** ******** methods *** ******* ****** used ******* *** **** applications (*.*. *** *****, well-lit ************) ** ** sufficiently ********.

Detection **** ********* **** ****** ***********

******* ** *** **** variables ********, ********* ****** is ********* **** ********* than *********** *** ********** on ****** **** **** been ********.

**** ** ****** ****** recognition, ***** ********* ** much ****** **** ***********.

Detection ********** ***********

******* ***** ********* ** primarily *********** ******* ** is ********* *******, ********* in ************ ******** *** weather **********, ** ****** subjects.

*** **** ****** ******* that ***** *** ********** are:

  • ***** ** ******
  • ******* *****
  • ******* ******
  • *******
  • ********
  • ***** *******

Angle ** ******

*** ***** ** ******* is *** **** ********, controllable ****** ** ***. Direct ****** **** ** higher ******** ******** ** sharper ******. **** ******** and ********** ****** ****** be ** ****** ** target ******** ** ********.

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******* ** ****, **** high-speed *** ************ ** highways (*.*. *******) *** mounted ******** ***** *** road ******* *** *** off ** *** ****.

Damaged / ***** ******

******* ** ***** ****** are *********** ** ******* learning *** *** *** because ***** ******* **** on ******* *****. **** commonly ***** ****** **********, missed *****, *** ******* reads ** ******** ******:

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***** **** ******** ********** are ****** ** ******* with ******* ** ***** plates ******* **** *** not ******* *** *****, damaged *** ***** ****** decrease *** ******* ** the ***** ******** ** the ******, ********** ********.

*******

***** **** *** ******* offer ******** *********** ** clear ******* **********; ****, snow, *** *** ************* impact ***** ********* *** capture ***********, ***** ******* LPR ********* ***********:

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******* ***** ** ******** climates, ****-******* ******** ****** with ******* *********** ***, completely ******** ***** ******* plates, *** **** ********* or ********** ******** **** plates:

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Low *** ******* ********

*** ***** *** *** 2 ***** **********; ******* lighting **** **********/**********, *** capturing ****** ***** ** read ******.

*** ******* **** ** be ****************** ** ***, ***** ***** ****** light ******* ****** ***** keeping *** ******* ***** visible:

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******* ********* *** *** light *** ** ********* camera ******* *****, ********* sufficient *****, *** *** so **** **** *** subject ******* **** ********:

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License ***** *********

******* ****** **** ****** throughout *** *****, **** differing *****, *********, ********, and ******/***********.

****** ****** ****** ****** formats (********* ***** **** black *********) *** *** much ****** ** ****:

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** ****** (*.*. ******* below) ******* **** **********, offering **** ***** ******* and ***** **** *** EU ******* ********, ***** can ************* ******** ********* accuracy, *** ******** **********:

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***** ** ********* ********** the ******* **** *** most **********, ******** ****-******* plates, ******** *** ******** brightly ********* **** ****/******, and **** ** *********:

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****** ********* ** ****** in *** ****** **** also **** ******** ********* than *****-***** **********, *** require ************* ****** ********** high ********.

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Regional ******* *** ******** ***********

**** *********** ** ***/**** provider, ** ** ********* to ***** *** ******* for ***'* ********** ******. This ** ********* ** a *******-**-******* *****. *******, even ****** * ******* country *********, ** ******* regions ****** **** ******* have ********* ***** ** plates, ******** ***** ** significantly ******* (*.*., *******).

Mobile *** ***** ***

*** ** ******* *** mobile *** ***** *************, however, *** ***** *** challenges *** *******:

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*** ******* ********** ** mobile *** ** * moving ****** ********, ***** complicates ***** *** ***** of *******. *******, *** plate ******* *** *********** methods *** *** ****.

Dedicated ******* ***

********* ******* *** ******* include *********** *** *******, software, *** ******* ***** management ******* **** * single ************. **** *** often ******** *** *********** light, ****-*****, *** **** traffic (*.*. *****-**** *******) applications.

***** ******* *** ********* marketed ** *****, ****-********, high-profile ***** *** *** not ********** ** *** mass ******.

Software **** ***

********-**** ******* ********* ***** lower-cost, ******** ** *******, commonly **** **** ******** algorithms *** ***-***** (***** 50-mph) ************.

********-**** ********* **** ******* flexibility ** ****** ****** but **** ********* ******** due ** ************ *******.

Wide ***** ****** (***** $*,*** ** $**,***+)

*** ****** ******* ****** from *** $*,*** ** $10,000+, *** **** ******* impact ***** ***:

  • ****** *******/********** ** **** typical ** *******
  • ******** ******** ******* *********
  • *** **** ****** ******** are ********* **** ***-***** ranging ** ** ** - ***** ****** **** speed (*** - ******), the ****** ***** *** far **** ********* **** for ******** *** ********.

Comments (3)

** ***** ** ******* to ******* * ********** chart ** **** ************ listing ***** *** ** OCR **. **** ********, local **. *****, ***.

Agree: 4
Disagree
Informative
Unhelpful
Funny

**** *******!

*** ****** ****** *** are *** ******* **** vendors ** ***** *******?

Agree
Disagree
Informative
Unhelpful
Funny

*'** **** ***** ***** (OpenALPR) *** ***** ******* a *********. ******** ******* for ********** (******, ***** or ****..** * ***** of ****) **** ** very ****. ***** ** is **** *****, * find *** ******* **** and ****** **** *** time ******** **** *** a *** **** ********* to ******.

Agree
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Informative
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Funny
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