Christopher Kermorvant

1.4k total citations
41 papers, 594 citations indexed

About

Christopher Kermorvant is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Christopher Kermorvant has authored 41 papers receiving a total of 594 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Computer Vision and Pattern Recognition, 25 papers in Artificial Intelligence and 5 papers in Media Technology. Recurrent topics in Christopher Kermorvant's work include Handwritten Text Recognition Techniques (33 papers), Natural Language Processing Techniques (20 papers) and Image Processing and 3D Reconstruction (14 papers). Christopher Kermorvant is often cited by papers focused on Handwritten Text Recognition Techniques (33 papers), Natural Language Processing Techniques (20 papers) and Image Processing and 3D Reconstruction (14 papers). Christopher Kermorvant collaborates with scholars based in France, Lebanon and Germany. Christopher Kermorvant's co-authors include Théodore Bluche, Jérôme Louradour, Hermann Ney, Thierry Paquet, Laurence Likforman-Sulem, Sébastien Adam, Clément Chatelain, Chafic Mokbel, Mohamed Faouzi BenZeghiba and Ronaldo Messina and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Pattern Recognition Letters.

In The Last Decade

Christopher Kermorvant

41 papers receiving 556 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Christopher Kermorvant France 14 475 281 115 54 40 41 594
Rajneesh Rani India 13 368 0.8× 109 0.4× 131 1.1× 58 1.1× 38 0.9× 73 519
Younes Akbari Qatar 13 291 0.6× 110 0.4× 74 0.6× 17 0.3× 47 1.2× 44 449
Adnan Ul-Hasan Pakistan 10 330 0.7× 139 0.5× 128 1.1× 26 0.5× 24 0.6× 20 401
Tetsushi Wakabayashi Japan 11 491 1.0× 194 0.7× 204 1.8× 15 0.3× 45 1.1× 77 581
Gaurav Harit India 13 316 0.7× 100 0.4× 100 0.9× 21 0.4× 55 1.4× 50 396
Savita Ahlawat India 7 289 0.6× 111 0.4× 160 1.4× 9 0.2× 45 1.1× 19 466
Jean-Marc Ogier France 13 421 0.9× 109 0.4× 124 1.1× 20 0.4× 18 0.5× 52 503
Marçal Rusiñol Spain 18 766 1.6× 273 1.0× 92 0.8× 33 0.6× 24 0.6× 49 861
Mohamed Elleuch Tunisia 11 278 0.6× 136 0.5× 136 1.2× 19 0.4× 73 1.8× 30 436
Mushtaq Ali Pakistan 11 202 0.4× 87 0.3× 43 0.4× 27 0.5× 7 0.2× 33 386

Countries citing papers authored by Christopher Kermorvant

Since Specialization
Citations

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

Fields of papers citing papers by Christopher Kermorvant

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Christopher Kermorvant

This figure shows the co-authorship network connecting the top 25 collaborators of Christopher Kermorvant. A scholar is included among the top collaborators of Christopher Kermorvant 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 Christopher Kermorvant. Christopher Kermorvant 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.
Vézina, Hélène, et al.. (2023). Large-scale genealogical information extraction from handwritten Quebec parish records. International Journal on Document Analysis and Recognition (IJDAR). 26(3). 255–272. 3 indexed citations
2.
Kermorvant, Christopher, et al.. (2022). Confidence Estimation for Object Detection in Document Images. Pattern Recognition Letters. 166. 31–37. 4 indexed citations
3.
Kermorvant, Christopher, et al.. (2022). Confidence Estimation for Object Detection in Document Images. SSRN Electronic Journal. 1 indexed citations
4.
Daille, Béatrice, et al.. (2020). Books of Hours. the First Liturgical Data Set for Text Segmentation.. Language Resources and Evaluation. 776–784. 1 indexed citations
5.
Kermorvant, Christopher, et al.. (2018). Learning to detect, localize and recognize many text objects in document images from few examples. International Journal on Document Analysis and Recognition (IJDAR). 21(3). 161–175. 6 indexed citations
6.
Bluche, Théodore, Christopher Kermorvant, Joan Puigcerver, et al.. (2017). Preparatory KWS Experiments for Large-Scale Indexing of a Vast Medieval Manuscript Collection in the HIMANIS Project. SPIRE - Sciences Po Institutional REpository. 311–316. 17 indexed citations
7.
Louradour, Jérôme, et al.. (2016). Learning text-line localization with shared and local regression neural networks. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations
8.
BenZeghiba, Mohamed Faouzi, Jérôme Louradour, & Christopher Kermorvant. (2015). Hybrid word/Part-of-Arabic-Word Language Models for arabic text document recognition. 671–675. 7 indexed citations
9.
Bluche, Théodore, Christopher Kermorvant, & Jérôme Louradour. (2015). Where to apply dropout in recurrent neural networks for handwriting recognition?. 681–685. 21 indexed citations
10.
Bluche, Théodore, Hermann Ney, Jérôme Louradour, & Christopher Kermorvant. (2015). Framewise and CTC training of Neural Networks for handwriting recognition. 81–85. 24 indexed citations
11.
Bluche, Théodore, Hermann Ney, & Christopher Kermorvant. (2015). The LIMSI handwriting recognition system for the HTRtS 2014 contest. 86–90. 6 indexed citations
12.
Messina, Ronaldo & Christopher Kermorvant. (2014). Over-Generative Finite State Transducer N-Gram for Out-of-Vocabulary Word Recognition. 8297. 212–216. 12 indexed citations
13.
Likforman-Sulem, Laurence, et al.. (2011). Variable length and context-dependent HMM letter form models for Arabic handwritten word recognition. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8297. 829708–829708. 7 indexed citations
14.
Louradour, Jérôme, et al.. (2011). The A2iA French handwriting recognition system at the Rimes-ICDAR2011 competition. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8297. 82970Y–82970Y. 30 indexed citations
15.
Mokbel, Chafic, et al.. (2011). Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 33(10). 2066–2080. 81 indexed citations
16.
Louradour, Jérôme & Christopher Kermorvant. (2011). Sample-Dependent Feature Selection for Faster Document Image Categorization. 3. 309–313. 1 indexed citations
17.
Kermorvant, Christopher, et al.. (2011). Automatic indexing of French handwritten census registers for probate geneaology. 51–58. 10 indexed citations
18.
Kermorvant, Christopher, et al.. (2010). The A2iA-Telecom ParisTech-UOB System for the ICDAR 2009 Handwriting Recognition Competition. 31. 247–252. 1 indexed citations
19.
Kermorvant, Christopher, et al.. (2009). Context-dependent HMM modeling using tree-based clustering for the recognition of handwritten words. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7534. 75340I–75340I. 6 indexed citations
20.
Bengio, Yoshua, et al.. (2003). Extracting hidden sense probabilities from bitexts. 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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