Harold Mouchère

1.8k total citations
39 papers, 391 citations indexed

About

Harold Mouchère is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Harold Mouchère has authored 39 papers receiving a total of 391 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Computer Vision and Pattern Recognition, 20 papers in Artificial Intelligence and 7 papers in Computational Theory and Mathematics. Recurrent topics in Harold Mouchère's work include Handwritten Text Recognition Techniques (25 papers), Natural Language Processing Techniques (11 papers) and Mathematics, Computing, and Information Processing (7 papers). Harold Mouchère is often cited by papers focused on Handwritten Text Recognition Techniques (25 papers), Natural Language Processing Techniques (11 papers) and Mathematics, Computing, and Information Processing (7 papers). Harold Mouchère collaborates with scholars based in France, United States and China. Harold Mouchère's co-authors include Christian Viard-Gaudin, Richard Zanibbi, Utpal Garain, Nicolas Normand, Éric Anquetil, Ting Zhang, Jinpeng Li, Anatole Lécuyer, Dorothea Blostein and Fabien Lotte and has published in prestigious journals such as SHILAP Revista de lepidopterología, Pattern Recognition and Pattern Recognition Letters.

In The Last Decade

Harold Mouchère

38 papers receiving 369 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Harold Mouchère France 12 275 165 98 34 33 39 391
Jiarui Liu China 9 101 0.4× 57 0.3× 83 0.8× 11 0.3× 19 0.6× 34 369
Baisen Cong China 7 344 1.3× 142 0.9× 148 1.5× 3 0.1× 61 1.8× 8 640
Melih Kandemir Germany 12 302 1.1× 328 2.0× 7 0.1× 35 1.0× 31 0.9× 32 513
Soham Chattopadhyay India 10 113 0.4× 245 1.5× 20 0.2× 4 0.1× 15 0.5× 13 367
Shah Nawaz Pakistan 11 173 0.6× 134 0.8× 26 0.3× 4 0.1× 22 0.7× 44 330
Dengdi Sun China 9 101 0.4× 152 0.9× 62 0.6× 8 0.2× 10 0.3× 47 325
Phen-Lan Lin Taiwan 12 236 0.9× 117 0.7× 8 0.1× 11 0.3× 64 1.9× 30 476
Michael Kohnen Germany 10 546 2.0× 288 1.7× 8 0.1× 5 0.1× 24 0.7× 34 690
Liyu Gong China 8 110 0.4× 126 0.8× 12 0.1× 9 0.3× 5 0.2× 19 316
Obaida M. Al-Hazaimeh Jordan 13 346 1.3× 195 1.2× 44 0.4× 9 0.3× 10 0.3× 46 589

Countries citing papers authored by Harold Mouchère

Since Specialization
Citations

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

Fields of papers citing papers by Harold Mouchère

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Harold Mouchère

This figure shows the co-authorship network connecting the top 25 collaborators of Harold Mouchère. A scholar is included among the top collaborators of Harold Mouchère 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 Harold Mouchère. Harold Mouchère 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.
Mouchère, Harold & Anna Zhu. (2024). Document Analysis and Recognition – ICDAR 2024 Workshops. Lecture notes in computer science. 2 indexed citations
2.
Bourreille, Arnaud, et al.. (2024). Influence of training and expertise on deep neural network attention and human attention during a medical image classification task. Journal of Vision. 24(4). 6–6. 1 indexed citations
3.
Nguyen, Cuong Tuan, et al.. (2024). A survey on handwritten mathematical expression recognition: The rise of encoder-decoder and GNN models. Pattern Recognition. 153. 110531–110531. 6 indexed citations
4.
Fasquel, Jean-Baptiste, et al.. (2023). Model-based inexact graph matching on top of DNNs for semantic scene understanding. Computer Vision and Image Understanding. 235. 103744–103744. 6 indexed citations
5.
Mouchère, Harold, et al.. (2023). Computing and evaluating saliency maps for image classification: a tutorial. Journal of Electronic Imaging. 32(2). 4 indexed citations
6.
Mebarki, Nasser, Sebastian Lang, Tobias Reggelin, et al.. (2022). Using Knowledge Graphs and Human-Centric Artificial Intelligence for Reconfigurable Supply Chains: A Research Framework. IFAC-PapersOnLine. 55(10). 1693–1698. 6 indexed citations
7.
Nich, Christophe, Julien Behr, Vincent Crenn, et al.. (2022). Applications of artificial intelligence and machine learning for the hip and knee surgeon: current state and implications for the future. International Orthopaedics. 46(5). 937–944. 17 indexed citations
8.
Feyeux, Magalie, Nicolas Normand, Laurent David, et al.. (2022). A time-lapse embryo dataset for morphokinetic parameter prediction. Data in Brief. 42. 108258–108258. 22 indexed citations
9.
Berre, Catherine Le, et al.. (2018). P161 Computer aided detection of Crohn’s disease small bowel lesions in wireless capsule endoscopy. Journal of Crohn s and Colitis. 12(supplement_1). S178–S179. 1 indexed citations
10.
Mouchère, Harold, et al.. (2017). Combining Speech and Handwriting Modalities for Mathematical Expression Recognition. IEEE Transactions on Human-Machine Systems. 47(2). 259–272. 10 indexed citations
11.
Mouchère, Harold, et al.. (2016). TUsing BLSTM for interpretation of 2-D languages. Case of handwritten mathematical expressions. 19(2-3). 135–157. 6 indexed citations
12.
Mouchère, Harold, Richard Zanibbi, Utpal Garain, & Christian Viard-Gaudin. (2016). Advancing the state of the art for handwritten math recognition: the CROHME competitions, 2011–2014. International Journal on Document Analysis and Recognition (IJDAR). 19(2). 173–189. 37 indexed citations
13.
14.
Zanibbi, Richard, Harold Mouchère, & Christian Viard-Gaudin. (2013). Evaluating structural pattern recognition for handwritten math via primitive label graphs. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8658. 865817–865817. 12 indexed citations
15.
Mouchère, Harold, et al.. (2012). ICFHR 2012 Competition on Recognition of On-Line Mathematical Expressions (CROHME 2012). 811–816. 21 indexed citations
16.
Zanibbi, Richard, et al.. (2011). Stroke-Based Performance Metrics for Handwritten Mathematical Expressions. 22 indexed citations
17.
Mouchère, Harold, et al.. (2009). A hybrid classifier for handwritten mathematical expression recognition. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7534. 753410–753410. 9 indexed citations
18.
Lotte, Fabien, Harold Mouchère, & Anatole Lécuyer. (2008). Pattern rejection strategies for the design of self-paced EEG-based Brain-Computer Interfaces. Proceedings - International Conference on Pattern Recognition. 1–5. 13 indexed citations
19.
Miclet, Laurent, et al.. (2007). De l'utilisation de la proportion analogique en apprentissage artificiel. SPIRE - Sciences Po Institutional REpository. 2 indexed citations
20.
Mouchère, Harold, et al.. (2005). On-line writer adaptation for handwriting recognition using fuzzy inference systems. 1075–1079 Vol. 2. 2 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

Breakdown of academic impact, for papers by Jiarui Liu Breakdown of academic impact, for papers by Baisen Cong Breakdown of academic impact, for papers by Melih Kandemir Breakdown of academic impact, for papers by Soham Chattopadhyay Breakdown of academic impact, for papers by Shah Nawaz Breakdown of academic impact, for papers by Dengdi Sun Breakdown of academic impact, for papers by Phen-Lan Lin Breakdown of academic impact, for papers by Michael Kohnen Breakdown of academic impact, for papers by Liyu Gong Breakdown of academic impact, for papers by Obaida M. Al-Hazaimeh
Rankless by CCL
2026