Mita Nasipuri

8.2k total citations
305 papers, 4.3k citations indexed

About

Mita Nasipuri is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence. According to data from OpenAlex, Mita Nasipuri has authored 305 papers receiving a total of 4.3k indexed citations (citations by other indexed papers that have themselves been cited), including 212 papers in Computer Vision and Pattern Recognition, 88 papers in Media Technology and 54 papers in Artificial Intelligence. Recurrent topics in Mita Nasipuri's work include Handwritten Text Recognition Techniques (91 papers), Face and Expression Recognition (74 papers) and Vehicle License Plate Recognition (60 papers). Mita Nasipuri is often cited by papers focused on Handwritten Text Recognition Techniques (91 papers), Face and Expression Recognition (74 papers) and Vehicle License Plate Recognition (60 papers). Mita Nasipuri collaborates with scholars based in India, United States and Poland. Mita Nasipuri's co-authors include Ram Sarkar, Debotosh Bhattacharjee, Mahantapas Kundu, Subhadip Basu, Nibaran Das, Dipak Kumar Basu, Pawan Kumar Singh, Jamuna Kanta Sing, Samir Malakar and Showmik Bhowmik and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and International Journal of Molecular Sciences.

In The Last Decade

Mita Nasipuri

295 papers receiving 4.0k citations

Author Peers

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

Author Last Decade Papers Cites
Mita Nasipuri 2.8k 1.4k 1.2k 438 307 305 4.3k
Ahmed Bouridane 3.1k 1.1× 538 0.4× 1.4k 1.2× 322 0.7× 136 0.4× 426 5.4k
M. Hanmandlu 2.2k 0.8× 729 0.5× 1.2k 1.0× 185 0.4× 66 0.2× 263 3.9k
Mingli Song 3.9k 1.4× 540 0.4× 1.5k 1.3× 191 0.4× 98 0.3× 237 5.5k
Luiz S. Oliveira 3.6k 1.3× 913 0.6× 3.6k 3.2× 1.5k 3.4× 222 0.7× 173 6.7k
Guoqiang Han 2.0k 0.7× 298 0.2× 1.4k 1.2× 196 0.4× 281 0.9× 172 3.9k
Zechao Li 5.0k 1.8× 888 0.6× 2.4k 2.1× 173 0.4× 143 0.5× 167 6.7k
Zhengming Ding 3.5k 1.3× 475 0.3× 3.5k 3.0× 347 0.8× 280 0.9× 144 5.7k
Jie Wen 3.4k 1.2× 889 0.6× 2.0k 1.7× 130 0.3× 211 0.7× 200 5.1k
Shu‐Tao Xia 2.4k 0.8× 973 0.7× 2.0k 1.7× 108 0.2× 204 0.7× 284 5.0k

Countries citing papers authored by Mita Nasipuri

Since Specialization
Citations

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

Fields of papers citing papers by Mita Nasipuri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mita Nasipuri

This figure shows the co-authorship network connecting the top 25 collaborators of Mita Nasipuri. A scholar is included among the top collaborators of Mita Nasipuri 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 Mita Nasipuri. Mita Nasipuri 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.
Das, Nibaran, et al.. (2023). An empirical study on analysis window functions for text-independent speaker recognition. International Journal of Speech Technology. 26(1). 211–220. 1 indexed citations
2.
Nasipuri, Mita, et al.. (2023). Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis. IEEE Transactions on Instrumentation and Measurement. 72. 1–13. 17 indexed citations
3.
Chatterjee, Piyali, et al.. (2022). Rule-Based Pruning and In Silico Identification of Essential Proteins in Yeast PPIN. Cells. 11(17). 2648–2648. 2 indexed citations
4.
Nasipuri, Mita, et al.. (2022). Variational Augmentation for Enhancing Historical Document Image Binarization✱. 1–7. 3 indexed citations
5.
Sahu, Geet, Ayan Seal, Debotosh Bhattacharjee, et al.. (2022). Trends and Prospects of Techniques for Haze Removal From Degraded Images: A Survey. IEEE Transactions on Emerging Topics in Computational Intelligence. 6(4). 762–782. 47 indexed citations
6.
Guha, Ritam, Manosij Ghosh, Pawan Kumar Singh, Ram Sarkar, & Mita Nasipuri. (2021). A Hybrid Swarm and Gravitation-based feature selection algorithm for handwritten Indic script classification problem. Complex & Intelligent Systems. 7(2). 823–839. 8 indexed citations
7.
Dey, R., et al.. (2021). Spoofing detection on hand images using quality assessment. Multimedia Tools and Applications. 80(19). 28603–28626. 3 indexed citations
8.
Banik, Debapriya, et al.. (2018). Suspicious-Region Segmentation From Breast Thermogram Using DLPE-Based Level Set Method. IEEE Transactions on Medical Imaging. 38(2). 572–584. 43 indexed citations
9.
Bhattacharjee, Debotosh, et al.. (2012). A novel approach for registration of 3D face images. IEEE-International Conference On Advances In Engineering, Science And Management. 1–7. 2 indexed citations
10.
Sarkar, Ram, et al.. (2011). Word extraction from unconstrained handwritten Bangla document images using Spiral Run Length Smearing Algorithm.. Indian International Conference on Artificial Intelligence. 32–46. 3 indexed citations
11.
Malakar, Samir, et al.. (2011). An improved offline handwritten character segmentation algorithm for Bangla script.. Indian International Conference on Artificial Intelligence. 71–90. 10 indexed citations
12.
Arora, Sandhya, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu, & Mahantapas Kundu. (2011). Complementary Features Combined in a MLP-based System to Recognize Handwritten Devnagari Character.. J. Inf. Hiding Multim. Signal Process.. 2. 6 indexed citations
13.
Sarkar, Ram, Subhadip Basu, Nibaran Das, et al.. (2009). Line Extraction from Unconstraint Handwritten Document Pages using Piece-wise Water-flow Technique.. Indian International Conference on Artificial Intelligence. 1861–1872. 4 indexed citations
14.
Das, Nibaran, Subhadip Basu, Ram Sarkar, Mahantapas Kundu, & Mita Nasipuri. (2009). Handwritten Bangla Compound character recognition: Potential challenges and probable solution.. Indian International Conference on Artificial Intelligence. 1901–1913. 12 indexed citations
15.
Garain, Utpal, et al.. (2007). On construction of a GeometryNet. 530–536. 6 indexed citations
16.
Roy, Kaushik, et al.. (2005). Comparison of the Multi Layer Perceptron and the Nearest Neighbor Classifier for Handwritten Numeral Recognition. Journal of information science and engineering. 21(6). 1247–1259. 9 indexed citations
17.
Basu, Subhadip, Nibaran Das, Ram Sarkar, et al.. (2005). An MLP based Approach for Recognition of Handwritten 'Bangla' Numerals. arXiv (Cornell University). 407–417. 17 indexed citations
18.
Sing, Jamuna Kanta, D. Basu, Mita Nasipuri, & Mahantapas Kundu. (2004). Center selection of RBF neural network based on modified k-means algorithm with point symmetry distance measure. Foundations of Computing and Decision Sciences. 247–266. 4 indexed citations
19.
Nasipuri, Mita, et al.. (1997). A New Microprocessor Based on Line Arrhythmia Diagnostic System. 1187–1191. 2 indexed citations
20.
Nasipuri, Mita, et al.. (1987). Microprocessor Based Real-Time Monitoring of Multiple ECG Signals. PubMed Central. 11. 822–827. 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.

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