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Topic · Computer Vision and Pattern Recognition

Handwritten Text Recognition Techniques

Handwritten Text Recognition Techniques is a research topic within Computer Vision and Pattern Recognition. Science Explorer counts 19k research works in it since 1951. 9.8% of them reached the world's top 10% most cited for their field and year.

This cluster of papers covers a wide range of topics related to handwriting recognition and text detection, including scene text recognition, document image analysis, neural networks for OCR, signature verification, text localization, and binarization techniques.

  • Handwriting Recognition
  • Text Detection
  • Scene Text Recognition
  • Document Image Analysis
  • Neural Networks
  • OCR Engine
  • Signature Verification
  • Text Localization
  • Convolutional Neural Networks
  • Binarization
Research works
19k
fractional, since 1951
In the world top 10%
1.8k
per year above
Top-10% rate
9.8%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+43%
the tick is no change
Largest producer
China
38% of the 10 largest, 2022-now
Handwritten Text Recognition Techniques

Which countries lead Handwritten Text Recognition Techniques research?

By volume, China and India publish the most (1.4k and 1.2k works in 2022-now).

By volume, 2022-now

  1. 1 China 1.4k works
  2. 2 India 1.2k works
  3. 3 United States 348 works
  4. 4 Indonesia 154 works
  5. 5 Japan 132 works
  6. 6 Germany 114 works
  7. 7 France 107 works
  8. 8 South Korea 87 works
  9. 9 Bangladesh 86 works
  10. 10 ?? 85 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: 38.1%India: 32.2%United States: 9.3%Indonesia: 4.1%6 others listed: 16.3%38%largest
China1,424 · 38.1%India1,202 · 32.2%United States348 · 9.3%Indonesia154 · 4.1%6 others listed610 · 16.3%

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

Which institutions lead Handwritten Text Recognition Techniques research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Handwritten Text Recognition Techniques research, followed by Amrita Vishwa Vidyapeetham and University of Science and Technology of China.

By volume, 2022-now

  1. 1 Chinese Academy of SciencesChina 57 works
  2. 2 Amrita Vishwa VidyapeethamIndia 51 works
  3. 3 University of Science and Technology of ChinaChina 36 works
  4. 4 Xinjiang UniversityChina 33 works
  5. 5 SRM Institute of Science and TechnologyIndia 31 works
  6. 6 Centre National de la Recherche ScientifiqueFrance 30 works
  7. 7 South China University of TechnologyChina 30 works
  8. 8 Vellore Institute of Technology UniversityIndia 28 works
  9. 9 Saveetha UniversityIndia 28 works
  10. 10 Savitribai Phule Pune UniversityIndia 25 works

Who are the leading researchers in Handwritten Text Recognition Techniques?

The most-cited researchers publishing on Handwritten Text Recognition Techniques include Yoshua Bengio, Geoffrey E. Hinton and Xiangyu Zhang.

  1. 1 Yoshua Bengio Canada 476k citations
  2. 2 Geoffrey E. Hinton Canada 454k citations
  3. 3 Xiangyu Zhang 327k citations
  4. 4 Trevor Hastie United States 301k citations
  5. 5 Yann LeCun United States 256k citations
  6. 6 Andrew Zisserman United Kingdom 255k citations
  7. 7 Vladimir N. Vapnik United States 227k citations
  8. 8 Kai You Wang China 224k citations
  9. 9 Trevor J. Darrell United States 206k citations
  10. 10 Jürgen Schmidhuber Switzerland 188k 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 Handwritten Text Recognition Techniques?

The most cited paper in Handwritten Text Recognition Techniques is "Gradient-based learning applied to document recognition" (Yann LeCun, Léon Bottou, Yoshua Bengio et al., 1998), with 59,921 citations. Next come "Backpropagation Applied to Handwritten Zip Code Recognition" (12,102) and "Visual pattern recognition by moment invariants" (7,575).

Most cited papers
#PaperReleasedCitations
1 Gradient-based learning applied to document recognition Yann LeCun, Léon Bottou, Yoshua Bengio et al. · Proceedings of the IEEE Open access 1998 59,921
2 Backpropagation Applied to Handwritten Zip Code Recognition Yann LeCun, Bernhard E. Boser, J. S. Denker et al. · Neural Computation Dec 1989 12,102
3 Visual pattern recognition by moment invariants Ming-Kuei Hu · IRE Transactions on Information Theory Feb 1962 7,575
4 LSTM: A Search Space Odyssey Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník et al. · IEEE Transactions on Neural Networks and Learning Systems Open access 14 Jul 2016 7,047
5 Statistical pattern recognition: a review Anil Kumar Jain, Peter Duin, Jianchang Mao · IEEE Transactions on Pattern Analysis and Machine Intelligence 2000 6,802
All 99 most cited papers, with filters and sorting

Where is Handwritten Text Recognition Techniques research done?

The largest centres of Handwritten Text Recognition Techniques research in 2022-now are Beijing (China), Chennai (India), Shanghai (China) and Wuhan (China). Among places with at least 20 works in it, it is an unusually large share of all research in Belagavi.

Largest cities, 2022-now

  1. 1 BeijingChina 280 works
  2. 2 ChennaiIndia 115 works
  3. 3 ShanghaiChina 109 works
  4. 4 WuhanChina 73 works
  5. 5 CoimbatoreIndia 73 works
  6. 6 GuangzhouChina 72 works
  7. 7 BengaluruIndia 72 works
  8. 8 PuneIndia 70 works
  9. 9 HangzhouChina 59 works
  10. 10 Xi'anChina 55 works

Where it is the local speciality

  1. BelagaviIN · 21.1 works11×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Handwritten Text Recognition Techniques than the world average.

See Handwritten Text Recognition Techniques on the map

Where is the best place to study Handwritten Text Recognition Techniques?

Among universities, judged by research, South China University of Technology, University of Science and Technology of China and Northwestern Polytechnical 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 23.15%fractional works in this field (log) →share of work in the world top 10% →Northwestern Polytec…South China Universi…Huazhong University …University of Scienc…
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
1South China University of TechnologyChina 79.428.9%6.0×30 +32.5%
2University of Science and Technology of ChinaChina 77.821.8%6.7×36 +77.1%
3Northwestern Polytechnical UniversityChina 64.232.5%2.5×13 +156.8%
4Huazhong University of Science and TechnologyChina 64.123.8%2.0×16 +43.7%
5Shanghai Jiao Tong UniversityChina 64.029.8%1.6×20 +26.9%
6Fudan UniversityChina 63.826.3%2.3×16 +196.8%
7Amrita Vishwa VidyapeethamIndia 62.08.3%21.6×51 +148.1%
8Zhejiang UniversityChina 59.319.0%1.8×21 +159.8%
9Symbiosis International UniversityIndia 54.021.0%6.8×10 +328.8%
10Peking UniversityChina 53.620.1%2.1×19 -3.1%

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 Handwritten Text Recognition Techniques, country by country

Is Handwritten Text Recognition Techniques research growing?

Output in 2022-2025 was 43% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Handwritten Text Recognition Techniques.

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 Handwritten Text Recognition Techniques as a percentage of every work published that year, 0.024% in 2024.

still counting0.020%0.040%Handwritten Text Recognition Techniques0.024% in 202519801990200020102020

Fractional works of Handwritten Text Recognition Techniques 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%Handwritten Text Recognition Techniques9.7% 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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