Journals / Gazi University Journal of Science Part A: Engineering and Innovation / 2026 / Volume: 12 Issue: 1

Novel Approach for Detecting the Number of Columns of a Résumé

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Publication Date
2025-03-26
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Furthermore, these findings could be extended to any unstructured textual data in any language and document format

📌 3 Key Takeaways

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Objective & Hypothesis:In recruitment processes, manually reviewing résumés is a highly time-consuming job.
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Key Findings & Data:However, because there is no consensus on a standard structure of résumés, i.e., each résumé has its own distinctive layout, column numbers, or text properties, an accurate extraction process becomes highly challenging.
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Conclusion & Impact:Furthermore, these findings could be extended to any unstructured textual data in any language and document format
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Information Extraction · Résumé Parsing · Clustering

Abstract

In recruitment processes, manually reviewing résumés is a highly time-consuming job. In order to reduce the cost of these reviews, Information Extraction tasks have been introduced to extract the structure of the document and the personal information contained within. However, because there is no consensus on a standard structure of résumés, i.e., each résumé has its own distinctive layout, column numbers, or text properties, an accurate extraction process becomes highly challenging. This study addresses a part of this problem. We focus on the problem of estimating the number of columns in résumés, as we experience in the further processes that knowing the number of columns facilitates the separation of the main sections of the résumés, hence the analysis of the finer subsections. We employ the coordinates of the text blocks that build up a résumé. We hypothesize that the coordinates of the text blocks carry information on the number of columns. We define the problem in a clustering context. We proposed a novel clustering approaches dedicated to finding the number of columns in a résumé by the separation of the text block coordinates. The experiments are conducted on a dataset of the résumés of real applicants in two languages: Turkish and English. The results reveal that hybrid approaches that use the intermediate methods perform better than the individual methods. Furthermore, these findings could be extended to any unstructured textual data in any language and document format

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Cite This Article

Bali, Y., Orman, G. K., Turhan, S. N. (2025). Novel Approach for Detecting the Number of Columns of a Résumé. Gazi University Journal of Science Part A: Engineering and Innovation, Volume: 12 Issue: 1. https://doi.org/10.54287/gujsa.1636051

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References (35)

  1. Adnan, K., & Akbar, R. (2019). An analytical study of information extraction from unstructured and multidimensional big data. Journal of Big Data, 6(1), 1-38. https://doi.org/10.1186/s40537-019-0254-8
  2. Alamelu, M., Kumar, D. S., Sanjana, R., Sree, J. S., Devi, A. S., & Kavitha, D. (2021, December). Resume validation and filtration using natural language processing. In 2021 10th International conference on internet of everything, microwave engineering, communication and networks (IEMECON) (pp. 1-5). IEEE. https://doi.org/10.1109/IEMECON53809.2021.9689075
  3. Chen, K., Yin, F., & Liu, C. L. (2013, August). Hybrid page segmentation with efficient whitespace rectangles extraction and grouping. In 2013 12th International Conference on Document Analysis and Recognition (pp. 958-962). IEEE. https://doi.org/10.1109/ICDAR.2013.194
  4. Chen, J., Zhang, C., & Niu, Z. (2018). A Two‐Step Resume Information Extraction Algorithm. Mathematical Problems in Engineering, 2018(1), 5761287. https://doi.org/10.1155/2018/5761287
  5. Çelik, D., & Elçi, A. (2012). An ontology-based information extraction approach for résumés. In Proceedings of the 2012 International Conference on Pervasive Computing and the Networked World ICPCA/SWS’12 (p. 165–179). Berlin, Heidelberg: Springer-Verlag. https://doi.org/10.1007/978-3-642-37015-1_14
  6. Das, P., Pandey, M., & Rautaray, S. S. (2018). A CV parser model using entity extraction process and big data tools. International Journal of Information Technology and Computer Science, 9(2), 21-31. https://doi.org/10.5815/ijitcs.2018.09.03
  7. Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996, August). A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd (Vol. 96, No. 34, pp. 226-231).
  8. Farkas, R., Dobó, A., Kurai, Z., Miklós, I., Nagy, Á., Vincze, V., & Zsibrita, J. (2014). Information extraction from Hungarian, English and German CVs for a career portal. In Mining Intelligence and Knowledge Exploration: Second International Conference, MIKE 2014, Cork, Ireland, December 10-12, 2014. Proceedings (pp. 333-341). Springer International Publishing. https://doi.org/10.1007/978-3-319-13817-6_32
  9. Gaur, B., Saluja, G. S., Sivakumar, H. B., & Singh, S. (2021). Semi-supervised deep learning based named entity recognition model to parse education section of resumes. Neural Computing and Applications, 33, 5705-5718. https://doi.org/10.1007/s00521-020-05351-2
  10. Holm, A. B. (2012). E-recruitment: Towards an ubiquitous recruitment process and candidate relationship management. German Journal of Human Resource Management, 26(3), 241-259 https://doi.org/10.1177/239700221202600303
  11. Ji, X., Zeng, J., Zhang, S., & Wu, C. (2010). Tag tree template for Web information and schema extraction. Expert Systems with applications, 37(12), 8492-8498. https://doi.org/10.1016/j.eswa.2010.05.027
  12. Joan, S. P. F., & Valli, S. (2019). A survey on text information extraction from born-digital and scene text images. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 89, 77–101. https://doi.org/10.1007/s40010-017-0478-y
  13. Kariyer.net (2025) www.kariyer.net
  14. Keskin, Ş. R., Balı, Y., Orman, G. K., Daniş, F. S., & Turhan, S. N. (2022, June). Determining Column Numbers in Résumés with Clustering. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 460-471). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-08337-2_38
  15. Kokoska, S., & Zwillinger, D. (2000). CRC standard probability and statistics tables and formulae. Crc Press.
  16. Liu, X., Gao, F., Zhang, Q., & Zhao, H. (2019). Graph convolution for multimodal information extraction from visually rich documents. arXiv preprint arXiv:1903.11279.
  17. Luo, Y., Zhang, H., Wang, Y., Wen, Y., & Zhang, X. (2018, November). ResumeNet: A learning-based framework for automatic resume quality assessment. In 2018 IEEE International Conference on Data Mining (ICDM) (pp. 307-316). IEEE. https://doi.org/10.1109/ICDM.2018.00046
  18. Mao, S., Rosenfeld, A., & Kanungo, T. (2003). Document structure analysis algorithms: a literature survey. In T. Kanungo, E. H. B. Smith, J. Hu, & P. B. Kantor (Eds.), Document Recognition and Retrieval (pp. 197 – 207). International Society for Optics and Photonics SPIE volume 5010. https://doi.org/10.1117/12.476326
  19. Mittal, V., Mehta, P., Relan, D., & Gabrani, G. (2020). Methodology for resume parsing and job domain prediction. Journal of Statistics and Management Systems, 23(7), 1265-1274. https://doi.org/10.1080/09720510.2020.1799583
  20. Qin, C., Zhu, H., Xu, T., Zhu, C., Ma, C., Chen, E., & Xiong, H. (2020). An enhanced neural network approach to person-job fit in talent recruitment. ACM Trans. Inf. Syst.,38 https://doi.org/10.1145/3376927
  21. Rao, G. A., Srinivas, G., Rao, K. V., & Reddy, P. P. (2018). A partial ratio and ratio based fuzzy-wuzzy procedure for characteristic mining of mathematical formulas from documents. IJSC—ICTACT J Soft Comput, 8(4), 1728-1732. https://doi.org/10.21917/ijsc.2018.0242
  22. Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics,20, 53–65 https://doi.org/10.1016/0377-0427(87)90125-7
  23. Roy, P. K., Chowdhary, S. S., & Bhatia, R. (2020). A machine learning approach for automation of resume recommendation system. Procedia Computer Science, 167, 2318-2327. https://doi.org/10.1016/j.procs.2020.03.284
  24. Sarawagi, S. (2008). Information extraction. Foundations and Trends® in Databases, 1, 261–377. https://doi.org/10.1561/1900000003
  25. Sinha, A.K., Amir Khusru Akhtar, M., Kumar, A. (2021). Resume Screening Using Natural Language Processing and Machine Learning: A Systematic Review. In: Swain, D., Pattnaik, P.K., Athawale, T. (eds) Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1311. Springer, Singapore. https://doi.org/10.1007/978-981-33-4859-2_21
  26. Sonar, S., & Bankar, B. (2012). Resume parsing with named entity clustering algorithm. Published paper, SVPM College of Engineering Baramati, Maharashtra.
  27. Tejaswini, K., Umadevi, V., Kadiwal, S. M., & Revanna, S. (2022). Design and development of machine learning based resume ranking system. Global Transitions Proceedings, 3(2), 371-375. https://doi.org/10.1016/j.gltp.2021.10.002
  28. Tobing, B. C. L., Suhendra, I. R., & Halim, C. (2019, June). Catapa resume parser: end to end Indonesian resume extraction. In Proceedings of the 2019 3rd international conference on natural language processing and information retrieval (pp. 68-74). https://doi.org/10.1145/3342827.3342832
  29. Xu, Q., Zhang, J., Zhu, Y., Li, B., Guan, D., & Wang, X. (2020). A blocklevel RNN model for resume block classification. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 5855–5857). https://doi.org/10.1109/BigData50022.2020.9377771
  30. Xuan, G., Zhang, W., & Chai, P. (2001, October). EM algorithms of Gaussian mixture model and hidden Markov model. In Proceedings 2001 international conference on image processing (Cat. No. 01CH37205) (Vol. 1, pp. 145-148). IEEE. https://doi.org/10.1109/ICIP.2001.958974
  31. Yakubovich, V., & Lup, D. (2006). Stages of the recruitment process and the referrer’s performance effect. Organization science, 17(6), 710–723. https://doi.org/10.1287/orsc.1060.0214
  32. Yasmin, F., Nur, M. I., & Arefin, M. S. (2020). Potential candidate selection using information extraction and skyline queries. In Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI-2019) (pp. 511-522). Springer International Publishing. https://doi.org/10.1007/978-3-030-43192-1_58
  33. Yu, K., Guan, G., & Zhou, M. (2005, June). Resume information extraction with cascaded hybrid model. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05) (pp. 499-506).
  34. Zaroor, A., Maree, M., & Sabha, M. (2017, May). A hybrid approach to conceptual classification and ranking of resumes and their corresponding job posts. In International Conference on Intelligent Decision Technologies (pp. 107-119). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-59421-7_10
  35. Zu, S., Wang, X., & Darren, S. (2019). Resume information extraction with a novel text block segmentation algorithm. Linguistics, 8, 29–48. doi:10. 5121/ijnlc.2019.8503. In Proceedings 2001 International Conference on Image Processing (Cat.No.01CH37205) (pp. 145–148 vol.1). volume 1.