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Vehicle License Plate Recognition

Vehicle License Plate Recognition is a research topic within Media Technology. Science Explorer counts 16k research works in it since 1950. 9.9% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the development and application of automatic license plate recognition systems, utilizing techniques such as deep learning, convolutional neural networks, and character segmentation. The research covers various aspects including vehicle identification, real-time recognition, and integration with intelligent transportation systems.

  • Automatic License Plate Recognition
  • ALPR
  • Vehicle Identification
  • Deep Learning
  • Convolutional Neural Networks
  • Character Segmentation
  • Intelligent Transportation System
  • Real-Time Recognition
  • Neural Network
  • Number Plate Detection
Research works
16k
fractional, since 1950
In the world top 10%
1.6k
per year above
Top-10% rate
9.9%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+71%
the tick is no change
Largest producer
China
38% of the 10 largest, 2022-now

Which countries lead Vehicle License Plate Recognition research?

By volume, China and India publish the most (2k and 1.7k works in 2022-now).

By volume, 2022-now

  1. 1 China 2k works
  2. 2 India 1.7k works
  3. 3 Indonesia 495 works
  4. 4 United States 245 works
  5. 5 ?? 146 works
  6. 6 Malaysia 127 works
  7. 7 Bangladesh 108 works
  8. 8 Türkiye 105 works
  9. 9 Iraq 92 works
  10. 10 South Korea 91 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 38.4%India: 33.9%Indonesia: 9.7%United States: 4.8%6 others listed: 13.2%38%largest
China1,953 · 38.4%India1,723 · 33.9%Indonesia495 · 9.7%United States245 · 4.8%6 others listed669 · 13.2%

Shares of the rows listed above, not of the whole node.

Which institutions lead Vehicle License Plate Recognition research?

By volume in 2022-now, Saveetha University publishes the most Vehicle License Plate Recognition research, followed by SRM Institute of Science and Technology and Vellore Institute of Technology University.

Who are the leading researchers in Vehicle License Plate Recognition?

The most-cited researchers publishing on Vehicle License Plate Recognition include Yoshua Bengio, Yann LeCun and Kai You Wang.

  1. 1 Yoshua Bengio Canada 476k citations
  2. 2 Yann LeCun United States 256k citations
  3. 3 Kai You Wang China 224k citations
  4. 4 Anil Kumar Jain United States 160k citations
  5. 5 Seyedali Mirjalili Australia 154k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Vehicle License Plate Recognition?

The most cited paper in Vehicle License Plate Recognition is "Reading digits in natural images with unsupervised feature learning" (Yuval Netzer, 2024), with 4,540 citations. Next come "Knapsack Problems: Algorithms and Computer Implementations" (3,161) and "Survey on deep learning with class imbalance" (3,018).

Most cited papers
#PaperReleasedCitations
1 Reading digits in natural images with unsupervised feature learning Yuval Netzer Open access 2024 4,540
2 Knapsack Problems: Algorithms and Computer Implementations David Connolly, Silvano Martello, Paolo Toth · Journal of the Operational Research Society Jun 1991 3,161
3 Survey on deep learning with class imbalance Justin M. Johnson, Taghi M. Khoshgoftaar · Journal Of Big Data Open access 19 Mar 2019 3,018
4 A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches Mikel Galar, Alberto Fernández, Edurne Barrenechea et al. · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) 15 Aug 2011 2,851
5 EAST: An Efficient and Accurate Scene Text Detector Xinyu Zhou, Cong Yao, He Wen et al. Conference paper Jul 2017 1,854
All 100 most cited papers, with filters and sorting

Where is Vehicle License Plate Recognition research done?

The largest centres of Vehicle License Plate Recognition research in 2022-now are Beijing (China), Chennai (India), Shanghai (China) and Xi'an (China). Among places with at least 20 works in it, it is an unusually large share of all research in Vijayawada.

Largest cities, 2022-now

  1. 1 BeijingChina 298 works
  2. 2 ChennaiIndia 269 works
  3. 3 ShanghaiChina 131 works
  4. 4 Xi'anChina 97 works
  5. 5 PuneIndia 97 works
  6. 6 CoimbatoreIndia 97 works
  7. 7 BengaluruIndia 95 works
  8. 8 GuangzhouChina 94 works
  9. 9 WuhanChina 93 works
  10. 10 NanjingChina 87 works

Where it is the local speciality

  1. VijayawadaIN · 44.9 works16×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Vehicle License Plate Recognition than the world average.

See Vehicle License Plate Recognition on the map

Where is the best place to study Vehicle License Plate Recognition?

Among universities, judged by research, Nanjing University of Information Science and Technology, Saveetha University and Vellore Institute of Technology University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 18.55%fractional works in this field (log) →share of work in the world top 10% →Nanjing University o…Beijing Jiaotong Uni…Vellore Institute of…Saveetha University
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Nanjing University of Information Science and TechnologyChina 64.032.0%4.6×10 +153.2%
2Saveetha UniversityIndia 63.66.9%18.5×96 +4014.3%
3Vellore Institute of Technology UniversityIndia 59.313.0%9.2×49 +44.8%
4Beijing Jiaotong UniversityChina 57.917.9%8.8×29 +86.3%
5SRM Institute of Science and TechnologyIndia 57.57.2%13.1×72 +233.8%
6Amrita Vishwa VidyapeethamIndia 56.910.1%16.6×48 +88.6%
7Southeast UniversityChina 55.920.2%3.0×24 +182.9%
8Jiangsu UniversityChina 51.932.1%1.9×8 +215.8%
9South China University of TechnologyChina 51.417.2%3.1×18 +26.9%
10Northwestern Polytechnical UniversityChina 50.828.9%1.9×12 +75.2%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Vehicle License Plate Recognition, country by country

Is Vehicle License Plate Recognition research growing?

Output in 2022-2025 was 71% higher than in 2018-2021, peaking in 2024. The fastest-growing topics are Vehicle License Plate Recognition.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Vehicle License Plate Recognition as a percentage of every work published that year, 0.037% in 2024.

still counting0.020%0.040%Vehicle License Plate Recognition0.027% in 202519801990200020102020

Fractional works of Vehicle License Plate Recognition over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Vehicle License Plate Recognition10.0% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.

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