Chayan Halder

437 total citations
23 papers, 181 citations indexed

About

Chayan Halder is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence. According to data from OpenAlex, Chayan Halder has authored 23 papers receiving a total of 181 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Computer Vision and Pattern Recognition, 14 papers in Media Technology and 8 papers in Artificial Intelligence. Recurrent topics in Chayan Halder's work include Handwritten Text Recognition Techniques (21 papers), Vehicle License Plate Recognition (14 papers) and Image Processing and 3D Reconstruction (10 papers). Chayan Halder is often cited by papers focused on Handwritten Text Recognition Techniques (21 papers), Vehicle License Plate Recognition (14 papers) and Image Processing and 3D Reconstruction (10 papers). Chayan Halder collaborates with scholars based in India, United States and Bangladesh. Chayan Halder's co-authors include Kaushik Roy, Sk Md Obaidullah, Nibaran Das, KC Santosh, A.B. Roy, Suparna Biswas, Anirban Das, Himadri Mukherjee, A. S. M. Jannatul Islam and Naim Ferdous and has published in prestigious journals such as Expert Systems with Applications, Multimedia Tools and Applications and International Journal of Machine Learning and Cybernetics.

In The Last Decade

Chayan Halder

21 papers receiving 171 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chayan Halder India 8 172 92 53 29 9 23 181
Sheikh Faisal Rashid Germany 11 239 1.4× 113 1.2× 81 1.5× 15 0.5× 4 0.4× 26 275
Jie-Bo Hou China 8 248 1.4× 135 1.5× 49 0.9× 6 0.2× 8 0.9× 11 275
Shi-Xue Zhang China 8 300 1.7× 145 1.6× 67 1.3× 6 0.2× 12 1.3× 9 332
Raid Saabni Israel 10 280 1.6× 108 1.2× 86 1.6× 26 0.9× 11 1.2× 25 290
Fouad Slimane Switzerland 11 334 1.9× 109 1.2× 120 2.3× 12 0.4× 12 1.3× 25 351
E. Augustin France 6 157 0.9× 49 0.5× 86 1.6× 17 0.6× 6 0.7× 7 172
Wei-Yi Pei China 6 258 1.5× 141 1.5× 37 0.7× 9 0.3× 5 0.6× 10 275
Aurélie Lemaître France 7 100 0.6× 31 0.3× 20 0.4× 11 0.4× 7 0.8× 21 116
R. Jayadevan India 8 290 1.7× 187 2.0× 79 1.5× 41 1.4× 20 2.2× 16 318
David Bridson United Kingdom 6 274 1.6× 41 0.4× 25 0.5× 12 0.4× 7 0.8× 6 277

Countries citing papers authored by Chayan Halder

Since Specialization
Citations

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

Fields of papers citing papers by Chayan Halder

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chayan Halder

This figure shows the co-authorship network connecting the top 25 collaborators of Chayan Halder. A scholar is included among the top collaborators of Chayan Halder 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 Chayan Halder. Chayan Halder 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.
Mukherjee, Himadri, et al.. (2024). Historical digit recognition using CNN: a study with English handwritten digits. Sadhana. 49(1). 1 indexed citations
2.
Biswas, Suparna, A.B. Roy, Chayan Halder, & Kaushik Roy. (2023). Personality analysis for handwritten documents: a case study with Bangla characters. Innovations in Systems and Software Engineering. 21(3). 841–854.
3.
Chatterjee, Somnath, et al.. (2022). Comparative study on the performance of the state-of-the-art CNN models for handwritten Bangla character recognition. Multimedia Tools and Applications. 82(11). 16929–16950. 2 indexed citations
4.
Halder, Chayan, et al.. (2022). A generalized line segmentation method for multi-script handwritten text documents. Expert Systems with Applications. 212. 118498–118498. 4 indexed citations
5.
6.
Obaidullah, Sk Md, KC Santosh, Nibaran Das, Chayan Halder, & Kaushik Roy. (2018). Handwritten Indic Script Identification in Multi-Script Document Images: A Survey. International Journal of Pattern Recognition and Artificial Intelligence. 32(10). 1856012–1856012. 17 indexed citations
7.
Halder, Chayan, Sk Md Obaidullah, KC Santosh, & Kaushik Roy. (2018). Content Independent Writer Identification on Bangla Script: A Document Level Approach. International Journal of Pattern Recognition and Artificial Intelligence. 32(9). 1856011–1856011. 11 indexed citations
8.
Obaidullah, Sk Md, Chayan Halder, KC Santosh, Nibaran Das, & Kaushik Roy. (2018). AUTOMATIC LINE-LEVEL SCRIPT IDENTIFICATION FROM HANDWRITTEN DOCUMENT IMAGES - A REGION-WISE CLASSIFICATION FRAMEWORK FOR INDIAN SUBCONTINENT. Malaysian Journal of Computer Science. 31(1). 63–84. 4 indexed citations
9.
Santosh, KC, et al.. (2017). Word-Level Multi-Script Indic Document Image Dataset and Baseline Results on Script Identification. 7(2). 81–94. 6 indexed citations
10.
Obaidullah, Sk Md, KC Santosh, Chayan Halder, Nibaran Das, & Kaushik Roy. (2017). Automatic Indic script identification from handwritten documents: page, block, line and word-level approach. International Journal of Machine Learning and Cybernetics. 10(1). 87–106. 24 indexed citations
11.
Obaidullah, Sk Md, Chayan Halder, KC Santosh, Nibaran Das, & Kaushik Roy. (2017). PHDIndic_11: page-level handwritten document image dataset of 11 official Indic scripts for script identification. Multimedia Tools and Applications. 77(2). 1643–1678. 47 indexed citations
12.
Obaidullah, Sk Md, et al.. (2016). Separating Indic Scripts with matra for Effective Handwritten Script Identification in Multi-Script Documents. International Journal of Pattern Recognition and Artificial Intelligence. 31(5). 1753003–1753003. 9 indexed citations
13.
Obaidullah, Sk Md, Chayan Halder, Nibaran Das, & Kaushik Roy. (2016). Bangla and Oriya Script Lines Identification from Handwritten Document Images in Tri-script Scenario. International Journal of Service Science Management Engineering and Technology. 7(1). 43–60. 2 indexed citations
14.
Obaidullah, Sk Md, Chayan Halder, Nibaran Das, & Kaushik Roy. (2016). A new dataset of word-level offline handwritten numeral images from four official Indic scripts and its benchmarking using image transform fusion. International Journal of Intelligent Engineering Informatics. 4(1). 1–1. 7 indexed citations
15.
Obaidullah, Sk Md, Nibaran Das, Chayan Halder, & Kaushik Roy. (2015). Indic script identification from handwritten document images — An unconstrained block-level approach. 2. 213–218. 8 indexed citations
16.
Halder, Chayan, Sk Md Obaidullah, & Kaushik Roy. (2015). Effect of writer information on Bangla handwritten character recognition. 1–4. 3 indexed citations
17.
Obaidullah, Sk Md, Chayan Halder, Nibaran Das, & Kaushik Roy. (2015). Numeral Script Identification from Handwritten Document Images. Procedia Computer Science. 54. 585–594. 16 indexed citations
18.
Obaidullah, Sk Md, et al.. (2015). Transform based approach for Indic script identification from handwritten document images. 3. 1–7. 8 indexed citations
19.
Halder, Chayan, et al.. (2012). Individuality of Bangla numerals. 2. 264–268. 2 indexed citations
20.
Halder, Chayan & Kaushik Roy. (2011). Word & Character Segmentation for Bangla Handwriting Analysis & Recognition. 243–246. 3 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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2026