Anuj Sharma

110 total papers · 1.6k total citations
53 papers, 962 citations indexed

About

Anuj Sharma is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Anuj Sharma has authored 53 papers receiving a total of 962 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Computer Vision and Pattern Recognition, 22 papers in Artificial Intelligence and 12 papers in Media Technology. Recurrent topics in Anuj Sharma's work include Handwritten Text Recognition Techniques (20 papers), Vehicle License Plate Recognition (12 papers) and Hand Gesture Recognition Systems (6 papers). Anuj Sharma is often cited by papers focused on Handwritten Text Recognition Techniques (20 papers), Vehicle License Plate Recognition (12 papers) and Hand Gesture Recognition Systems (6 papers). Anuj Sharma collaborates with scholars based in India, United States and United Kingdom. Anuj Sharma's co-authors include Kalpana Dahiya, Vinod Kumar Chauhan, Rajendra Kumar Sharma, Spyros Deftereos, Andreas Persidis, Christos Andronis, Rajesh Kumar, Sukhdeep Singh, Rajesh Kumar and Rajiv Kumar Sharma and has published in prestigious journals such as SHILAP Revista de lepidopterología, European Journal of Operational Research and Magnetic Resonance in Medicine.

In The Last Decade

Anuj Sharma

49 papers receiving 922 citations

Hit Papers

Problem formulations and ... 2018 2026 2020 2023 2018 100 200 300

Author Peers

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

Author Last Decade Papers Cites
Anuj Sharma 268 225 173 150 102 53 962
Karla Brkić 361 1.3× 457 2.0× 66 0.4× 94 0.6× 57 0.6× 38 1.2k
Junjie Huang 572 2.1× 294 1.3× 191 1.1× 122 0.8× 36 0.4× 91 1.2k
P.R. Innocent 404 1.5× 328 1.5× 85 0.5× 79 0.5× 59 0.6× 32 917
Petr Somol 369 1.4× 473 2.1× 93 0.5× 136 0.9× 62 0.6× 47 938
Pavel Paclı́k 578 2.2× 503 2.2× 178 1.0× 92 0.6× 45 0.4× 24 1.1k
Xiang‐Jun Shen 366 1.4× 284 1.3× 97 0.6× 108 0.7× 36 0.4× 71 1.2k
Lei Yang 268 1.0× 154 0.7× 153 0.9× 146 1.0× 64 0.6× 96 1.0k
Amr Badr 133 0.5× 365 1.6× 39 0.2× 131 0.9× 73 0.7× 70 1.0k
Shin‐Jye Lee 309 1.2× 243 1.1× 241 1.4× 105 0.7× 73 0.7× 58 945
Sayan Chakraborty 403 1.5× 199 0.9× 90 0.5× 51 0.3× 51 0.5× 93 1.0k

Countries citing papers authored by Anuj Sharma

Since Specialization
Citations

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

Fields of papers citing papers by Anuj Sharma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Anuj Sharma

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

All Works

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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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