Showmik Bhowmik

893 total citations
26 papers, 540 citations indexed

About

Showmik Bhowmik is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence. According to data from OpenAlex, Showmik Bhowmik has authored 26 papers receiving a total of 540 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Computer Vision and Pattern Recognition, 12 papers in Media Technology and 4 papers in Artificial Intelligence. Recurrent topics in Showmik Bhowmik's work include Handwritten Text Recognition Techniques (24 papers), Vehicle License Plate Recognition (12 papers) and Image Retrieval and Classification Techniques (11 papers). Showmik Bhowmik is often cited by papers focused on Handwritten Text Recognition Techniques (24 papers), Vehicle License Plate Recognition (12 papers) and Image Retrieval and Classification Techniques (11 papers). Showmik Bhowmik collaborates with scholars based in India, Greece and United States. Showmik Bhowmik's co-authors include Ram Sarkar, Mita Nasipuri, Samir Malakar, Manosij Ghosh, David Doermann, Pawan Kumar Singh, Subhadip Basu, Ergina Kavallieratou, Mahantapas Kundu and Kushal Kanti Ghosh and has published in prestigious journals such as IEEE Transactions on Image Processing, IEEE Transactions on Instrumentation and Measurement and Neural Computing and Applications.

In The Last Decade

Showmik Bhowmik

26 papers receiving 499 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Showmik Bhowmik India 14 396 174 166 34 21 26 540
Satoshi Naoi Japan 13 516 1.3× 165 0.9× 164 1.0× 31 0.9× 29 1.4× 81 649
Guangfeng Lin China 15 487 1.2× 106 0.6× 172 1.0× 36 1.1× 27 1.3× 45 650
Yan‐Ming Zhang China 12 389 1.0× 82 0.5× 266 1.6× 30 0.9× 61 2.9× 31 603
Jean-Marc Ogier France 13 421 1.1× 124 0.7× 109 0.7× 18 0.5× 20 1.0× 52 503
Ergina Kavallieratou Greece 19 853 2.2× 257 1.5× 148 0.9× 45 1.3× 48 2.3× 77 949
Marçal Rusiñol Spain 18 766 1.9× 92 0.5× 273 1.6× 24 0.7× 33 1.6× 49 861
Amit Choudhary India 8 291 0.7× 156 0.9× 123 0.7× 48 1.4× 16 0.8× 24 494
Siwei Feng United States 11 700 1.8× 171 1.0× 289 1.7× 15 0.4× 58 2.8× 27 927
Rajneesh Rani India 13 368 0.9× 131 0.8× 109 0.7× 38 1.1× 58 2.8× 73 519
Alceu de Souza Britto Brazil 15 299 0.8× 72 0.4× 288 1.7× 20 0.6× 64 3.0× 70 585

Countries citing papers authored by Showmik Bhowmik

Since Specialization
Citations

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

Fields of papers citing papers by Showmik Bhowmik

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Showmik Bhowmik

This figure shows the co-authorship network connecting the top 25 collaborators of Showmik Bhowmik. A scholar is included among the top collaborators of Showmik Bhowmik 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 Showmik Bhowmik. Showmik Bhowmik 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.
Bhowmik, Showmik, et al.. (2024). DSANet: dilated spatial attention network for the detection of text, non-text and touching components in unconstrained handwritten documents. Neural Computing and Applications. 36(27). 16959–16976. 1 indexed citations
2.
Bhowmik, Showmik. (2023). Utilization of relative context for text non-text region classification in offline documents using multi-scale dilated convolutional neural network. Multimedia Tools and Applications. 83(9). 26751–26774. 4 indexed citations
3.
Bhowmik, Showmik. (2023). Document Layout Analysis. SpringerBriefs in computer science. 3 indexed citations
4.
5.
Bhowmik, Showmik, et al.. (2020). BINYAS: a complex document layout analysis system. Multimedia Tools and Applications. 80(6). 8471–8504. 15 indexed citations
6.
Ghosh, Manosij, Kushal Kanti Ghosh, Showmik Bhowmik, & Ram Sarkar. (2020). Coalition game based feature selection for text non-text separation in handwritten documents using LBP based features. Multimedia Tools and Applications. 80(2). 3229–3249. 3 indexed citations
7.
Singh, Pawan Kumar, et al.. (2020). Language-invariant novel feature descriptors for handwritten numeral recognition. The Visual Computer. 37(7). 1781–1803. 13 indexed citations
8.
Malakar, Samir, et al.. (2020). Understanding contents of filled-in Bangla form images. Multimedia Tools and Applications. 80(3). 3529–3570. 10 indexed citations
9.
Malakar, Samir, et al.. (2020). Handwritten word recognition using lottery ticket hypothesis based pruned CNN model: a new benchmark on CMATERdb2.1.2. Neural Computing and Applications. 32(18). 15209–15220. 12 indexed citations
10.
Guha, Ritam, Kushal Kanti Ghosh, Showmik Bhowmik, & Ram Sarkar. (2020). Mutually Informed Correlation Coefficient (MICC) - a New Filter Based Feature Selection Method. 54–58. 16 indexed citations
11.
Basu, Arpan, et al.. (2020). U-Net versus Pix2Pix: a comparative study on degraded document image binarization. Journal of Electronic Imaging. 29(6). 13 indexed citations
12.
Malakar, Samir, et al.. (2020). Offline music symbol recognition using Daisy feature and quantum Grey wolf optimization based feature selection. Multimedia Tools and Applications. 79(43-44). 32011–32036. 11 indexed citations
13.
Malakar, Samir, Manosij Ghosh, Showmik Bhowmik, Ram Sarkar, & Mita Nasipuri. (2019). A GA based hierarchical feature selection approach for handwritten word recognition. Neural Computing and Applications. 32(7). 2533–2552. 163 indexed citations
14.
Bhowmik, Showmik, Ram Sarkar, Mita Nasipuri, & David Doermann. (2018). Text and non-text separation in offline document images: a survey. International Journal on Document Analysis and Recognition (IJDAR). 21(1-2). 1–20. 45 indexed citations
15.
Bhowmik, Showmik, et al.. (2018). GiB: A ${G}$ ame Theory ${I}$ nspired ${B}$ inarization Technique for Degraded Document Images. IEEE Transactions on Image Processing. 28(3). 1443–1455. 32 indexed citations
16.
Pallavi, Pallavi, et al.. (2018). Handwritten Bangla word recognition using negative refraction based shape transformation. Journal of Intelligent & Fuzzy Systems. 35(2). 1765–1777. 15 indexed citations
17.
Bhowmik, Showmik, et al.. (2017). Text and non-text recognition using modified HOG descriptor. 64–68. 14 indexed citations
18.
Singh, Pawan Kumar, et al.. (2016). A Harmony Search Based Wrapper Feature Selection Method for Holistic Bangla Word Recognition. Procedia Computer Science. 89. 395–403. 22 indexed citations
19.
Bhowmik, Showmik, et al.. (2014). Handwritten Bangla Word Recognition Using HOG Descriptor. 193–197. 21 indexed citations
20.
Bhowmik, Showmik, Samir Malakar, Ram Sarkar, & Mita Nasipuri. (2014). Handwritten Bangla Word Recognition Using Elliptical Features. 257–261. 18 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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