Canjie Luo

2.6k total citations · 1 hit paper
24 papers, 1.2k citations indexed

About

Canjie Luo is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence. According to data from OpenAlex, Canjie Luo has authored 24 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Computer Vision and Pattern Recognition, 7 papers in Media Technology and 3 papers in Artificial Intelligence. Recurrent topics in Canjie Luo's work include Handwritten Text Recognition Techniques (24 papers), Image Processing and 3D Reconstruction (11 papers) and Image Retrieval and Classification Techniques (10 papers). Canjie Luo is often cited by papers focused on Handwritten Text Recognition Techniques (24 papers), Image Processing and 3D Reconstruction (11 papers) and Image Retrieval and Classification Techniques (10 papers). Canjie Luo collaborates with scholars based in China, United States and Australia. Canjie Luo's co-authors include Lianwen Jin, Zenghui Sun, Yuanzhi Zhu, Shuaitao Zhang, Tianwei Wang, Xiaoxue Chen, Yuliang Liu, Sheng Zhang, Yuliang Liu and Yongpan Wang and has published in prestigious journals such as IEEE Transactions on Image Processing, ACM Computing Surveys and Pattern Recognition.

In The Last Decade

Canjie Luo

24 papers receiving 1.1k citations

Hit Papers

align trajectories

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.

MORAN: A Multi-Object Rectified Attention Network for scene text recognition

2019 284 citations Canjie Luo, Lianwen Jin et al. Pattern Recognition profile →

Peers

Canjie Luo

CVPR

MT

AI

IS

SP

Hwalsuk Lee South Korea

Pengyuan Lyu China

Lluís Gómez Spain

Jon Almazán Spain

Songxuan Lai China

Mohammad Tanvir Parvez Saudi Arabia

Gyeonghwan Kim South Korea

Christian Viard-Gaudin France

Marçal Rusiñol Spain

Xiaobao Guo Singapore

Hwalsuk Lee South Korea

Canjie Luo

743 ×0.7

CVPR

300 ×0.7

MT

204 ×0.8

AI

45 ×1.2

IS

21 ×0.6

SP

Citations per year, relative to Canjie Luo Canjie Luo (= 1×) peers Hwalsuk Lee

Countries citing papers authored by Canjie Luo

Since Specialization

Citations

This map shows the geographic impact of Canjie Luo'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 Canjie Luo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Canjie Luo more than expected).

Fields of papers citing papers by Canjie Luo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Canjie Luo. 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 Canjie Luo. The network helps show where Canjie Luo may publish in the future.

Co-authorship network of co-authors of Canjie Luo

This figure shows the co-authorship network connecting the top 25 collaborators of Canjie Luo. A scholar is included among the top collaborators of Canjie Luo 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 Canjie Luo. Canjie Luo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Luo, Canjie, Lianwen Jin, & Jingdong Chen. (2022). SimAN: Exploring Self-Supervised Representation Learning of Scene Text via Similarity-Aware Normalization. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 1029–1038. 16 indexed citations

2.

Zhang, Jiaxin, et al.. (2022). Marior: Margin Removal and Iterative Content Rectification for Document Dewarping in the Wild. Proceedings of the 30th ACM International Conference on Multimedia. 2805–2815. 17 indexed citations

3.

Luo, Canjie, Yuanzhi Zhu, Lianwen Jin, Zhe Li, & Dezhi Peng. (2022). SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text. IEEE Transactions on Neural Networks and Learning Systems. 34(11). 8503–8515. 13 indexed citations

4.

Chen, Xiaoxue, et al.. (2021). 自然场景文本检测与识别的深度学习方法. Journal of Image and Graphics. 26(6). 1330–1367. 3 indexed citations

5.

Liu, Yuliang, Tong He, Hao Chen, et al.. (2021). Exploring the Capacity of an Orderless Box Discretization Network for Multi-orientation Scene Text Detection. International Journal of Computer Vision. 129(6). 1972–1992. 19 indexed citations

6.

Chen, Xiaoxue, Lianwen Jin, Yuanzhi Zhu, Canjie Luo, & Tianwei Wang. (2021). Text Recognition in the Wild. ACM Computing Surveys. 54(2). 1–35. 95 indexed citations

7.

Luo, Canjie, et al.. (2021). Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild. International Journal of Computer Vision. 129(4). 960–976. 21 indexed citations

8.

Wang, Tianwei, Yuanzhi Zhu, Lianwen Jin, et al.. (2021). Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter. 5969–5978. 6 indexed citations

9.

Liu, Chongyu, Yuliang Liu, Lianwen Jin, et al.. (2020). EraseNet: End-to-End Text Removal in the Wild. IEEE Transactions on Image Processing. 29. 8760–8775. 21 indexed citations

10.

Wang, Tianwei, Yuanzhi Zhu, Lianwen Jin, et al.. (2020). Decoupled Attention Network for Text Recognition. Proceedings of the AAAI Conference on Artificial Intelligence. 34(7). 12216–12224. 161 indexed citations

11.

Luo, Canjie, et al.. (2020). STAN: A sequential transformation attention-based network for scene text recognition. Pattern Recognition. 111. 107692–107692. 36 indexed citations

12.

Luo, Canjie, Yuanzhi Zhu, Lianwen Jin, & Yongpan Wang. (2020). Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition. 13743–13752. 57 indexed citations

13.

Liu, Yuliang, Tong He, Hao Chen, et al.. (2019). Exploring the Capacity of Sequential-free Box Discretization Network for Omnidirectional Scene Text Detection.. arXiv (Cornell University). 2 indexed citations

14.

Sun, Zenghui, et al.. (2019). EPAN: Effective parts attention network for scene text recognition. Neurocomputing. 376. 202–213. 18 indexed citations

15.

Luo, Canjie, et al.. (2019). Attention After Attention: Reading Text in the Wild with Cross Attention. 274–280. 3 indexed citations

16.

Ding, Errui, Jingtuo Liu, Dìmosthenis Karatzas, et al.. (2019). ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text - RRC-ArT. 1571–1576. 95 indexed citations

17.

Luo, Canjie, Lianwen Jin, & Zenghui Sun. (2019). MORAN: A Multi-Object Rectified Attention Network for scene text recognition. Pattern Recognition. 90. 109–118. 284 indexed citations breakdown →

18.

Liu, Yuliang, Lianwen Jin, Shuaitao Zhang, Canjie Luo, & Sheng Zhang. (2019). Curved scene text detection via transverse and longitudinal sequence connection. Pattern Recognition. 90. 337–345. 178 indexed citations

19.

He, Mengchao, Yuliang Liu, Zhibo Yang, et al.. (2018). ICPR2018 Contest on Robust Reading for Multi-Type Web Images. 7–12. 30 indexed citations

20.

Zhang, Sheng, Yuliang Liu, Lianwen Jin, & Canjie Luo. (2018). Feature Enhancement Network: A Refined Scene Text Detector. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 30 indexed citations

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.

Explore authors with similar magnitude of impact