Sukalpa Chanda

80 total papers · 612 total citations
34 papers, 351 citations indexed

About

Sukalpa Chanda is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Sukalpa Chanda has authored 34 papers receiving a total of 351 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Computer Vision and Pattern Recognition, 13 papers in Artificial Intelligence and 6 papers in Media Technology. Recurrent topics in Sukalpa Chanda's work include Handwritten Text Recognition Techniques (24 papers), Image Processing and 3D Reconstruction (12 papers) and Natural Language Processing Techniques (8 papers). Sukalpa Chanda is often cited by papers focused on Handwritten Text Recognition Techniques (24 papers), Image Processing and 3D Reconstruction (12 papers) and Natural Language Processing Techniques (8 papers). Sukalpa Chanda collaborates with scholars based in India, Norway and United States. Sukalpa Chanda's co-authors include Umapada Pal, Katrin Franke, Srikanta Pal, Michael Blumenstein, Oriol Ramos Terrades, Fumitaka Kimura, Tetsushi Wakabayashi, Nabin Sharma, Alain Rakotomamonjy and Saumik Bhattacharya and has published in prestigious journals such as IEEE Transactions on Cybernetics, Neurocomputing and Image and Vision Computing.

In The Last Decade

Sukalpa Chanda

30 papers receiving 334 citations

Author Peers

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

Author						Papers	Cites
Sukalpa Chanda	299	104	98	34	23	34	351
Mallikarjun Hangarge	283 0.9×	142 1.4×	80 0.8×	21 0.6×	14 0.6×	32	336
Wenchao Zhang	289 1.0×	37 0.4×	53 0.5×	13 0.4×	52 2.3×	24	348
Praveen Krishnan	309 1.0×	76 0.7×	171 1.7×	40 1.2×	20 0.9×	22	399
Muhammet Baştan	228 0.8×	18 0.2×	61 0.6×	10 0.3×	18 0.8×	25	293
Bart Lamiroy	315 1.1×	43 0.4×	42 0.4×	16 0.5×	18 0.8×	41	366
Gloria M. Díaz	213 0.7×	49 0.5×	104 1.1×	8 0.2×	7 0.3×	35	331
Jawad Hasan Alkhateeb	288 1.0×	135 1.3×	139 1.4×	34 1.0×	23 1.0×	27	361
Xiaodong Yu	261 0.9×	25 0.2×	62 0.6×	14 0.4×	28 1.2×	28	312
Mingqi Gao	228 0.8×	39 0.4×	64 0.7×	8 0.2×	16 0.7×	23	293
Trung-Hieu Le	246 0.8×	43 0.4×	33 0.3×	13 0.4×	5 0.2×	20	324

Countries citing papers authored by Sukalpa Chanda

Since Specialization

Citations

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

Fields of papers citing papers by Sukalpa Chanda

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sukalpa Chanda

This figure shows the co-authorship network connecting the top 25 collaborators of Sukalpa Chanda. A scholar is included among the top collaborators of Sukalpa Chanda 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 Sukalpa Chanda. Sukalpa Chanda 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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