Lianwen Jin

14.0k total citations · 1 hit paper
311 papers, 7.4k citations indexed

About

Lianwen Jin is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Lianwen Jin has authored 311 papers receiving a total of 7.4k indexed citations (citations by other indexed papers that have themselves been cited), including 268 papers in Computer Vision and Pattern Recognition, 80 papers in Artificial Intelligence and 53 papers in Media Technology. Recurrent topics in Lianwen Jin's work include Handwritten Text Recognition Techniques (175 papers), Image Processing and 3D Reconstruction (72 papers) and Image Retrieval and Classification Techniques (66 papers). Lianwen Jin is often cited by papers focused on Handwritten Text Recognition Techniques (175 papers), Image Processing and 3D Reconstruction (72 papers) and Image Retrieval and Classification Techniques (66 papers). Lianwen Jin collaborates with scholars based in China, United States and Australia. Lianwen Jin's co-authors include Yuliang Liu, Canjie Luo, Zecheng Xie, Dapeng Tao, Songxuan Lai, Xuelong Li, Lingyu Liang, Weixin Yang, Zenghui Sun and Zhuoyao Zhong and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Expert Systems with Applications.

In The Last Decade

Lianwen Jin

293 papers receiving 7.2k citations

Hit Papers

MORAN: A Multi-Object Rec... 2019 2026 2021 2023 2019 50 100 150 200 250

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Lianwen Jin China 44 6.0k 1.9k 1.8k 589 513 311 7.4k
Gang Hua United States 52 8.0k 1.3× 1.0k 0.5× 2.4k 1.3× 289 0.5× 521 1.0× 251 10.0k
Alberto Del Bimbo Italy 47 8.4k 1.4× 912 0.5× 1.8k 1.0× 584 1.0× 633 1.2× 532 9.9k
Baocai Yin China 39 4.1k 0.7× 769 0.4× 1.4k 0.8× 303 0.5× 331 0.6× 446 7.8k
Xiaohui Shen United States 42 8.7k 1.4× 1.3k 0.7× 1.5k 0.8× 355 0.6× 382 0.7× 99 9.9k
Ling‐Yu Duan China 47 6.8k 1.1× 747 0.4× 2.0k 1.1× 527 0.9× 1.2k 2.3× 241 7.8k
Ashutosh Saxena United States 42 4.7k 0.8× 704 0.4× 2.2k 1.2× 892 1.5× 1.6k 3.1× 136 8.3k
Ying Wu United States 50 8.4k 1.4× 888 0.5× 3.1k 1.7× 1.3k 2.2× 1.4k 2.7× 277 10.7k
Alexander G. Hauptmann United States 44 5.2k 0.9× 665 0.3× 3.4k 1.9× 245 0.4× 446 0.9× 149 8.0k
Dapeng Tao China 45 3.8k 0.6× 884 0.5× 1.2k 0.7× 230 0.4× 820 1.6× 198 5.0k
Xi Li China 39 4.0k 0.7× 463 0.2× 1.5k 0.8× 218 0.4× 627 1.2× 333 6.4k

Countries citing papers authored by Lianwen Jin

Since Specialization
Citations

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

Fields of papers citing papers by Lianwen Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lianwen Jin

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

All Works

20 of 20 papers shown
1.
Díaz, Moises, et al.. (2025). Online Signature Verification based on the Lagrange formulation with 2D and 3D robotic models. Pattern Recognition. 164. 111581–111581. 1 indexed citations
2.
Yang, Zhenhua, et al.. (2025). MegaHan97K: A large-scale dataset for mega-category Chinese character recognition with over 97K categories. Pattern Recognition. 167. 111757–111757.
3.
Liu, Yang, et al.. (2025). Datasets for large language models: a comprehensive survey. Artificial Intelligence Review. 58(12). 1 indexed citations
4.
Li, Hongliang, et al.. (2025). Smaller But Better: Unifying Layout Generation with Smaller Large Language Models. International Journal of Computer Vision. 133(7). 3891–3917. 1 indexed citations
5.
Zhu, Yuanzhi, Dezhi Peng, Zhenhua Yang, et al.. (2024). HierCode: A lightweight hierarchical codebook for zero-shot Chinese text recognition. Pattern Recognition. 158. 110963–110963. 6 indexed citations
6.
Peng, Dezhi, Chongyu Liu, Yuliang Liu, & Lianwen Jin. (2024). ViTEraser: Harnessing the Power of Vision Transformers for Scene Text Removal with SegMIM Pretraining. Proceedings of the AAAI Conference on Artificial Intelligence. 38(5). 4468–4477. 2 indexed citations
7.
Zhang, Ning, et al.. (2024). M2Doc: A Multi-Modal Fusion Approach for Document Layout Analysis. Proceedings of the AAAI Conference on Artificial Intelligence. 38(7). 7233–7241. 1 indexed citations
8.
Jin, Lianwen, et al.. (2024). Irregular text block recognition via decoupling visual, linguistic, and positional information. Pattern Recognition. 153. 110516–110516. 2 indexed citations
9.
Li, Hongliang, Dezhi Peng, & Lianwen Jin. (2024). EGO-LM: An efficient, generic, and out-of-the-box language model for handwritten text recognition. Pattern Recognition. 159. 111130–111130. 1 indexed citations
10.
11.
Quan, Yuhui, et al.. (2023). Enhancing texture representation with deep tracing pattern encoding. Pattern Recognition. 146. 109959–109959. 10 indexed citations
12.
Li, Zhe, et al.. (2023). A tree-based model with branch parallel decoding for handwritten mathematical expression recognition. Pattern Recognition. 149. 110220–110220. 3 indexed citations
13.
Liu, Chongyu, et al.. (2023). A robust and efficient algorithm for Chinese historical document analysis and recognition. National Science Review. 10(6). nwad115–nwad115. 2 indexed citations
14.
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
15.
Liu, Yuliang, et al.. (2019). Arbitrarily Shaped Scene Text Detection With a Mask Tightness Text Detector. IEEE Transactions on Image Processing. 29. 2918–2930. 44 indexed citations
16.
Lin, Luojun, Lingyu Liang, & Lianwen Jin. (2019). Regression Guided by Relative Ranking Using Convolutional Neural Network (R3 CNN) for Facial Beauty Prediction. IEEE Transactions on Affective Computing. 13(1). 122–134. 32 indexed citations
17.
Peng, Dezhi, et al.. (2018). Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture. 2528–2533. 47 indexed citations
18.
Yang, Weixin, Terry Lyons, Hao Ni, et al.. (2017). Leveraging the Path Signature for Skeleton-based Human Action Recognition.. arXiv (Cornell University). 18 indexed citations
19.
Gao, Yan, et al.. (2011). Chinese handwriting quality evaluation based on analysis of recognition confidence. 221–225. 6 indexed citations
20.
Jin, Lianwen. (2010). Design and implementation of home education robot with character recognition function based on WiFi wireless network. Jisuanji yingyong yanjiu. 1 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.

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