Deepak Mane
Impact in
- Media Technology top 10%
- Vehicle License Plate Recognition
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- Handwritten Text Recognition Techniques
- Advanced Neural Network Applications
- Video Surveillance and Tracking Methods
Papers in
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- Vehicle License Plate Recognition 6
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- Advanced Neural Network Applications 6
- Handwritten Text Recognition Techniques 5
- Co-authors
- U. V. KulkarniSwati ShindePatrick SchaumontBilgiday YuceSandeep KadamKailash ShawRajesh PrasadParikshit N. Mahalle
- Journals
- Scientific Reports (1 paper)Sustainable Computing Informatics and Systems (1 paper)IEEE Access (1 paper)Big Data and Cognitive Computing (1 paper)Image Analysis & Stereology (1 paper)
- Partner nations
- IndiaUnited StatesAustralia
In The Last Decade
Deepak Mane
28 papers receiving 119 citations
Peers
Comparison fields: 5 of 54
- Media Technology 36
- Computer Vision and Pattern Recognition 61
- Health Information Management 11
- Artificial Intelligence 60
- Health Informatics 2
Countries citing papers authored by Deepak Mane
This map shows the geographic impact of Deepak Mane'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 Deepak Mane with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deepak Mane more than expected).
Fields of papers citing papers by Deepak Mane
This network shows the impact of papers produced by Deepak Mane. 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 Deepak Mane. The network helps show where Deepak Mane may publish in the future.
Co-authorship network
The 19 scholars most cited alongside Deepak Mane, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 0 | |
| 8 | 2024 | 1 | |
| 9 | 2024 | 1 | |
| 10 | 2023 | 2 | |
| 11 | 2023 | 0 | |
| 12 | 2023 | 5 | |
| 13 | 2023 | 7 | |
| 14 | 2022 | 5 | |
| 15 | 2022 | 18 | |
| 16 | 2022 | 1 | |
| 17 | 2022 | 5 | |
| 18 | 2021 | 11 | |
| 19 | A Survey On Deep Learning Approaches For Vehicle And Number Plate Detection | 2019 | 8 |
| 20 | Survey On Text Categorization Using Sentiment Analysis | 2019 | 7 |
About Deepak Mane
Deepak Mane is a scholar working on Media Technology, Computer Vision and Pattern Recognition, Health Information Management, Artificial Intelligence and Neurology, having authored 40 papers that have together received 157 indexed citations. Recurring topics across this work include Vehicle License Plate Recognition (6 papers), Advanced Neural Network Applications (6 papers), Fuzzy Logic and Control Systems (5 papers), Handwritten Text Recognition Techniques (5 papers), Brain Tumor Detection and Classification (4 papers), AI in cancer detection (4 papers), Network Security and Intrusion Detection (4 papers) and Neural Networks and Applications (4 papers). The work is most often cited by research in Media Technology (36 citations), Computer Vision and Pattern Recognition (61 citations), Health Information Management (11 citations), Artificial Intelligence (60 citations) and Health Informatics (2 citations). Deepak Mane has collaborated with scholars based in India, United States and Australia. Frequent co-authors include U. V. Kulkarni, Swati Shinde, Patrick Schaumont, Bilgiday Yuce, Sandeep Kadam, Kailash Shaw, Rajesh Prasad, Parikshit N. Mahalle, Rakesh K. Jain and Chang-Wook Lee. Their work appears in journals such as Scientific Reports, Sustainable Computing Informatics and Systems, IEEE Access, Big Data and Cognitive Computing and Image Analysis & Stereology.
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.