Rayson Laroca

1.4k total citations · 1 hit paper
23 papers, 658 citations indexed

About

Rayson Laroca is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence. According to data from OpenAlex, Rayson Laroca has authored 23 papers receiving a total of 658 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 15 papers in Media Technology and 2 papers in Artificial Intelligence. Recurrent topics in Rayson Laroca's work include Vehicle License Plate Recognition (15 papers), Handwritten Text Recognition Techniques (6 papers) and Advanced Vision and Imaging (4 papers). Rayson Laroca is often cited by papers focused on Vehicle License Plate Recognition (15 papers), Handwritten Text Recognition Techniques (6 papers) and Advanced Vision and Imaging (4 papers). Rayson Laroca collaborates with scholars based in Brazil and Portugal. Rayson Laroca's co-authors include David Menotti, William Robson Schwartz, Gabriel Resende Gonçalves, Luiz A. Zanlorensi, Evair Borges Severo, Luiz S. Oliveira, Eduardo Todt, Diego Rafael Lucio, Eduardo Cunha de Almeida and Rodrigo Minetto and has published in prestigious journals such as Scientific Reports, IEEE Access and IEEE Signal Processing Letters.

In The Last Decade

Rayson Laroca

21 papers receiving 620 citations

Hit Papers

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What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

2018 325 citations Rayson Laroca, Evair Borges Severo et al. arXiv (Cornell University) profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Rayson Laroca	525	471	104	68	52	23	658
Gabriel Resende Gonçalves	449 0.9×	414 0.9×	99 1.0×	54 0.8×	43 0.8×	10	562
Luiz A. Zanlorensi	407 0.8×	346 0.7×	86 0.8×	46 0.7×	42 0.8×	12	537
Shyang‐Lih Chang	530 1.0×	516 1.1×	183 1.8×	113 1.7×	25 0.5×	8	680
Zezhi Chen	484 0.9×	155 0.3×	39 0.4×	69 1.0×	97 1.9×	26	595
Lele Xie	307 0.6×	216 0.5×	63 0.6×	50 0.7×	29 0.6×	14	410
Jakub Sochor	505 1.0×	175 0.4×	71 0.7×	75 1.1×	179 3.4×	16	611
Zhe Dai	240 0.5×	98 0.2×	61 0.6×	37 0.5×	101 1.9×	29	398
Sumedh Gupte	511 1.0×	127 0.3×	78 0.8×	88 1.3×	166 3.2×	3	629
Huaiyu Li	300 0.6×	101 0.2×	83 0.8×	77 1.1×	113 2.2×	7	415
Andrzej Ruta	356 0.7×	192 0.4×	15 0.1×	46 0.7×	33 0.6×	14	442

Countries citing papers authored by Rayson Laroca

Since Specialization

Citations

This map shows the geographic impact of Rayson Laroca's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Rayson Laroca with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rayson Laroca more than expected).

Fields of papers citing papers by Rayson Laroca

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rayson Laroca. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Rayson Laroca. The network helps show where Rayson Laroca may publish in the future.

Co-authorship network of co-authors of Rayson Laroca

This figure shows the co-authorship network connecting the top 25 collaborators of Rayson Laroca. A scholar is included among the top collaborators of Rayson Laroca based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Rayson Laroca. Rayson Laroca is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Schwartz, William Robson, et al.. (2025). Toward Advancing License Plate Super-Resolution in Real-World Scenarios: A Dataset and Benchmark. Journal of the Brazilian Computer Society. 31(1). 435–449. 1 indexed citations

Laroca, Rayson, et al.. (2025). Improving Small Drone Detection Through Multi-Scale Processing and Data Augmentation. arXiv (Cornell University). 1–8.

Laroca, Rayson, et al.. (2025). LPLC: A Dataset for License Plate Legibility Classification. arXiv (Cornell University). 1–6.

Laroca, Rayson, et al.. (2024). Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven Approach. 1–6. 3 indexed citations

Laroca, Rayson, et al.. (2024). Dense video captioning using unsupervised semantic information. Journal of Visual Communication and Image Representation. 107. 104385–104385. 1 indexed citations

Laroca, Rayson, et al.. (2024). Super-Resolution Towards License Plate Recognition. 78–87. 1 indexed citations

Laroca, Rayson, et al.. (2023). Super-resolution of license plate images using attention modules and sub-pixel convolution layers. Computers & Graphics. 113. 69–76. 11 indexed citations

Laroca, Rayson, et al.. (2023). Tell me what you see: A zero-shot action recognition method based on natural language descriptions. Multimedia Tools and Applications. 83(9). 28147–28173. 4 indexed citations

Laroca, Rayson, et al.. (2023). DACov: a deeper analysis of data augmentation on the computed tomography segmentation problem. Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization. 11(5). 1724–1741. 1 indexed citations

10.

Laroca, Rayson, et al.. (2023). Super-Resolution Towards License Plate Recognition. 28–34. 2 indexed citations

11.

Laroca, Rayson, et al.. (2023). Do We Train on Test Data? The Impact of Near-Duplicates on License Plate Recognition. 1–8. 7 indexed citations

12.

Laroca, Rayson, et al.. (2022). Global Semantic Descriptors for Zero-Shot Action Recognition. IEEE Signal Processing Letters. 29. 1843–1847. 3 indexed citations

13.

Laroca, Rayson, et al.. (2022). On the Cross-dataset Generalization in License Plate Recognition. arXiv (Cornell University). 166–178. 36 indexed citations

14.

Zanlorensi, Luiz A., et al.. (2022). A new periocular dataset collected by mobile devices in unconstrained scenarios. Scientific Reports. 12(1). 8 indexed citations

15.

Laroca, Rayson, et al.. (2021). Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks. IEEE Access. 9. 101065–101077. 18 indexed citations

16.

Laroca, Rayson, et al.. (2021). Towards Image-Based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach. IEEE Access. 9. 67569–67584. 32 indexed citations

17.

Laroca, Rayson & David Menotti. (2020). Automatic License Plate Recognition: An Efficient and Layout-Independent System Based on the YOLO Detector. 15–21. 3 indexed citations

18.

Laroca, Rayson, et al.. (2019). Convolutional neural networks for automatic meter reading. Journal of Electronic Imaging. 28(1). 1–1. 58 indexed citations

19.

Lucio, Diego Rafael, Rayson Laroca, Evair Borges Severo, Alceu S. Britto, & David Menotti. (2018). Fully Connected Networks and Generative Neural Networks Applied to Sclera Segmentation.. arXiv (Cornell University). 2 indexed citations

20.

Laroca, Rayson, Evair Borges Severo, Luiz A. Zanlorensi, et al.. (2018). A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector. arXiv (Cornell University). 1–10. 325 indexed citations breakdown →

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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