Detection of Newspaper Layouts Using YOLO12

Atul Kumar; Gurpreet Singh Lehal

doi:10.62762/TMI.2025.846033

Volume 2, Issue 2, ICCK Transactions on Machine Intelligence

Volume 2, Issue 2, 2026

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ICCK Transactions on Machine Intelligence, Volume 2, Issue 2, 2026: 77-87

Free to Read | Research Article | 09 February 2026

Detection of Newspaper Layouts Using YOLO12

Atul Kumar 1 *

Gurpreet Singh Lehal 2

1 Department of Computer Science, R.G.M. Government College, Joginder Nagar 175015, India

2 Department of Computer Science, Punjabi University, Patiala 147002, India

* Corresponding Author: Atul Kumar, [email protected]

DOI: 10.62762/TMI.2025.846033

ARK: ark:/57805/tmi.2025.846033

Received: 10 November 2025, Accepted: 31 December 2025, Published: 09 February 2026

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Abstract

This study presents a robust and scalable method for automatic layout detection in digitized newspapers to facilitate efficient knowledge extraction and information retrieval. A custom dataset comprising annotated newspaper images in English, Hindi, and other languages was developed, with layout regions categorized into five primary classes. An enhanced YOLOv12 object detection model was trained on this dataset and evaluated using the mean Average Precision (mAP) metric across various Intersection over Union (IoU) thresholds. The model achieved a mAP@50 of 0.88, demonstrating strong detection performance and outperforming several stateof-the-art object detection models in the same task. The findings validate the effectiveness of the proposed approach in handling multilingual, structurally diverse newspaper formats. This research provides a practical framework for integrating automated layout analysis into digital archiving systems, OCR pipelines, and media monitoring applications. It also supports broader efforts to digitize historical print media and improve accessibility to regional content, thereby enabling enhanced research, journalism, and public engagement.

Graphical Abstract

Detection of Newspaper Layouts Using YOLO12

Keywords

newspapers

YOLO

segmentation

layout analysis

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

AI Use Statement

The authors declare that no generative AI was used in the preparation of this manuscript.

Ethical Approval and Consent to Participate

Not applicable.

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APA Style

Kumar, A., & Lehal, G. S. (2026). Detection of Newspaper Layouts Using YOLO12. ICCK Transactions on Machine Intelligence, 2(2), 77–87. https://doi.org/10.62762/TMI.2025.846033

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TY  - JOUR
AU  - Kumar, Atul
AU  - Lehal, Gurpreet Singh
PY  - 2026
DA  - 2026/02/09
TI  - Detection of Newspaper Layouts Using YOLO12
JO  - ICCK Transactions on Machine Intelligence
T2  - ICCK Transactions on Machine Intelligence
JF  - ICCK Transactions on Machine Intelligence
VL  - 2
IS  - 2
SP  - 77
EP  - 87
DO  - 10.62762/TMI.2025.846033
UR  - https://www.icck.org/article/abs/TMI.2025.846033
KW  - newspapers
KW  - YOLO
KW  - segmentation
KW  - layout analysis
AB  - This study presents a robust and scalable method for automatic layout detection in digitized newspapers to facilitate efficient knowledge extraction and information retrieval. A custom dataset comprising annotated newspaper images in English, Hindi, and other languages was developed, with layout regions categorized into five primary classes. An enhanced YOLOv12 object detection model was trained on this dataset and evaluated using the mean Average Precision (mAP) metric across various Intersection over Union (IoU) thresholds. The model achieved a mAP@50 of 0.88, demonstrating strong detection performance and outperforming several stateof-the-art object detection models in the same task. The findings validate the effectiveness of the proposed approach in handling multilingual, structurally diverse newspaper formats. This research provides a practical framework for integrating automated layout analysis into digital archiving systems, OCR pipelines, and media monitoring applications. It also supports broader efforts to digitize historical print media and improve accessibility to regional content, thereby enabling enhanced research, journalism, and public engagement.
SN  - 3068-7403
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Kumar2026Detection,
  author = {Atul Kumar and Gurpreet Singh Lehal},
  title = {Detection of Newspaper Layouts Using YOLO12},
  journal = {ICCK Transactions on Machine Intelligence},
  year = {2026},
  volume = {2},
  number = {2},
  pages = {77-87},
  doi = {10.62762/TMI.2025.846033},
  url = {https://www.icck.org/article/abs/TMI.2025.846033},
  abstract = {This study presents a robust and scalable method for automatic layout detection in digitized newspapers to facilitate efficient knowledge extraction and information retrieval. A custom dataset comprising annotated newspaper images in English, Hindi, and other languages was developed, with layout regions categorized into five primary classes. An enhanced YOLOv12 object detection model was trained on this dataset and evaluated using the mean Average Precision (mAP) metric across various Intersection over Union (IoU) thresholds. The model achieved a mAP@50 of 0.88, demonstrating strong detection performance and outperforming several stateof-the-art object detection models in the same task. The findings validate the effectiveness of the proposed approach in handling multilingual, structurally diverse newspaper formats. This research provides a practical framework for integrating automated layout analysis into digital archiving systems, OCR pipelines, and media monitoring applications. It also supports broader efforts to digitize historical print media and improve accessibility to regional content, thereby enabling enhanced research, journalism, and public engagement.},
  keywords = {newspapers, YOLO, segmentation, layout analysis},
  issn = {3068-7403},
  publisher = {Institute of Central Computation and Knowledge}
}

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