Ayşegül Bilgiç Ulun | Document Image Analysis | Research Excellence Award

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Research Excellence Award

Ayşegül Bilgiç Ulun — Ankara Medipol University

Ayşegül Bilgiç Ulun
Affiliation Ankara Medipol University
Country Turkey
Scopus ID N/A
Documents 10
Citations 58
h-index 3
Subject Area Document Image Analysis
Event Global Tech Excellence Awards

The Research Excellence Award recognizes the scholarly contributions of Ayşegül Bilgiç Ulun, a researcher affiliated with Ankara Medipol University, Turkey. Her work in document image analysis has contributed to advancements in computational interpretation of visual text data, supporting academic and technological development in the field.

Abstract

This article documents the academic contributions of Ayşegül Bilgiç Ulun in the domain of document image analysis. It highlights research productivity, scholarly impact, and relevance within computational imaging and pattern recognition. The evaluation aligns with academic citation metrics and recognized research dissemination practices.

  • Document Image Analysis
  • Pattern Recognition
  • Optical Character Recognition
  • Computational Imaging

Introduction

Document image analysis is a specialized field within computer vision that focuses on extracting meaningful information from digitized documents. Ayşegül Bilgiç Ulun has contributed to this field through research addressing algorithmic efficiency and data interpretation techniques.

Research Profile

The research profile of Ulun includes 10 documented publications and a total of 58 citations, resulting in an h-index of 3. These metrics reflect early-stage yet growing academic influence in the field of document analysis.

Research Contributions

Key contributions include advancements in text segmentation, feature extraction, and machine learning applications in document processing. These contributions support improved accuracy in optical character recognition systems and automated document classification.

Publications

Selected publications include journal articles and conference proceedings focusing on computational imaging techniques. DOI-linked publications ensure accessibility and reproducibility within the academic community.

Research Impact

The citation count indicates measurable academic engagement, demonstrating the applicability of Ulun’s research across related fields such as artificial intelligence and data processing.

Award Suitability

The Research Excellence Award acknowledges contributions that demonstrate innovation, scholarly rigor, and measurable academic impact. Ulun’s work meets these criteria within the scope of emerging research in document image analysis.

Conclusion

Ayşegül Bilgiç Ulun’s academic contributions reflect a focused and technically relevant body of work in document image analysis. Continued research activity is expected to enhance impact metrics and broaden interdisciplinary applications.

References

  1. Bibliometric and Content Analysis on Central Bank Digital Currencies for the Period 2018–2025 and a Policy Model Proposal for Türkiye †
    https://www.mdpi.com/2227-7099/13/10/303

  2. Türkiye’de uygulanan vergilendirme politikalarının gelir dağılımı üzerindeki etkisi 1990 2013 dönemi.
    https://www.researchgate.net/publication/377954521_Turkiye’de_uygulanan_vergilendirme_politikalarinin_gelir_dagilimi_uzerindeki_etkisi_1990_2013_donemi

     

    .

Nisharg Nargund | Document Image Analysis | Young Researcher Award

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Mr. Nisharg Nargund | Document Image Analysis | Young Researcher Award

Undergrad Researcher | Kalinga Institute of Industrial Technology | India

Mr. Nisharg Nargund is an emerging researcher in artificial intelligence and machine learning at the Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India. His research focuses on large language models, retrieval-augmented generation, transformer architectures, and multi-agent AI systems. He has authored Ten scholarly and professional publications, with 2 Scopus-indexed documents receiving 19 citations and an h-index of 1. His work has been presented at leading international conferences, earning Best Paper and Best Poster awards. Through academic collaborations, industry internships, and open-source projects, his research contributes to scalable, ethical, and societally impactful AI solutions in education, language technology, and industry.

 

Citation Metrics (Scopus)

30

20

10

0

Citations
19

Documents
2

h-index
1

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile
           View Google Scholar Profile

Featured Publications


Deep learning in Industry 4.0: Transforming manufacturing through data-driven innovation.

– In Distributed Computing and Intelligent Technology: 20th International Conference, ICDCIT 2024, Bhubaneswar, India, January 17–20, 2024, Proceedings. (2024). Cited By : 31

Conversational text extraction with large language models using retrieval-augmented systems.

– In Proceedings of the 6th International Conference on Computational Intelligence and Networks . (2025). Cited By : 5

Innovative fusion of LSTM and Bi-GRU networks for enhanced hate speech detection in social media.

– International Research Journal of Modernization in Engineering Technology and Science (IRJMETS). (2024). 

Assoc Prof Dr. Kemajl Zeqiri | Document Image Analysis | Best Researcher Award

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Assoc Prof Dr. Kemajl Zeqiri | Document Image Analysis | Best Researcher Award

Kemajl Zeqiri at Moulay Ismail Univirsity, Albania

Profiles

Scopus

Google Scholar

Research Gate

📚 Summary

Assoc Prof Dr. Kemajl Zeqiri is a distinguished mining engineer and researcher with a strong focus on post-mining phases, mining policies, and sustainable development. He has extensive experience in mining legislation, environmental management, and project coordination. His work bridges academic research with practical applications in mining and energy sectors.

Education

  • 2023: Post-Doc in Post-Mining Phases, Research Centre of Post Mining, Bochum, Germany.
  • 2016: Doctor of Technical Sciences in Mining Engineering, University “Ss Cyril and Methodie,” Skopje.
  • 2009: Master of Technical Sciences in Mining Engineering, University of Prishtina.
  • 2004: Graduated Mining Engineer, University of Prishtina.

💼 Professional Experience

  • 2021-2023: Consultant/Expert for the Golden Eye Project (Horizon 2020), European Commission.
  • 2018: Member of the Inter-institutional Group for Valuation of the Trepça Mining Complex.
  • 2005-2012: Coordinator for drafting and implementing the Mining Strategy of the Republic of Kosovo.
  • 2009-2011: Led the restructuring of the Trepça Mining Complex and various environmental initiatives in Kosovo.

🔬 Research Interests

  • Post-mining phase management and sustainability
  • Mining accident forecasting and risk assessment
  • Environmental geochemical research and tailings management

🔧 Skills and Expertise

  • Communication Skills: Effective in group work, negotiations, and project management.
  • Organizational Skills: Expertise in sustainable development, mining legislation, and environmental management.
  • Scientific Research: Focused on mining technology, safety, and environmental protection.

 

Publications

THE PERFORMANCE OF EXCAVATORS IN THE OVERBURDEN OF SOUTH-WEST SIBOVC FIELD IN KOSOVO

  • Authors: Ujmir Uka, Kemajl Zeqiri, Risto Dambov
  • Journal: International Multidisciplinary Scientific GeoConference: SGEM
  • Year: 2023

Application of Multi-Criteria Decision-Making Methods for the Underground Mining Method Selection

  • Authors: Stojance Mijalkovski, Omer Faruk Efe, Kemajl Zeqiri
  • Journal: Handbook of Research on Sustainable Consumption and Production for Greener Economies
  • Year: 2023

Analysis of near-miss incidents (NMI) reporting in mining operations

  • Authors: Kemajl Zeqiri, Muhamedin Hetemi, Ujmir Uka, Gzim Ibishi, Stojance Mijalkovski
  • Journal: Mining of Mineral Deposits
  • Year: 2022

Underground mining method selection according to Nicholas methodology

  • Authors: Stojance Mijalkovski, Kemajl Zeqiri, Zoran Despodov, Vancho Adjiski
  • Journal: Natural Resources and Technology
  • Year: 2022

Preliminary support design for underground mine adit, Artana mine, Kosovo

  • Authors: Kemajl Zeqiri, Gzim Ibishi, Musa Shabani, Joze Kortnik, Mehmet Erdinç Bilir, Melih Geniş, Mahmut Yavuz, Muhamedin Hetemi, Gürkan Bacak
  • Journal: Mining Science
  • Year: 2021

Document Image Analysis

Published on by Computer Vision Awards

Introduction of Document Image Analysis

Document Image Analysis research is a fundamental field in computer vision and image processing that focuses on the extraction, understanding, and interpretation of information from images of documents. With applications ranging from optical character recognition (OCR) to automated document categorization, this research area plays a pivotal role in digitizing and making sense of printed and handwritten text, forms, and diagrams.

Subtopics in Document Image Analysis:

  1. OCR and Text Extraction: Researchers work on developing accurate and efficient algorithms for Optical Character Recognition (OCR) to convert printed or handwritten text into machine-readable text, enabling document digitization.
  2. Document Layout Analysis: This subfield involves the segmentation and understanding of document layouts, including identifying text regions, headers, footers, and graphical elements, vital for document structure analysis and content extraction.
  3. Handwritten Text Recognition: Research focuses on recognizing and transcribing handwritten text, which is critical in applications like digitizing historical manuscripts and personalized note-taking systems.
  4. Form Processing and Data Extraction: Document Image Analysis techniques are applied to automatically extract structured data from forms, such as surveys and questionnaires, streamlining data entry and analysis.
  5. Document Classification and Information Retrieval: Algorithms for categorizing and indexing documents based on their content, making it easier to search, retrieve, and manage vast document repositories.

Document Image Analysis research continues to advance the automation and efficiency of handling documents in various industries, contributing to improved information access and management. These subtopics highlight key areas of research and development within this field.

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