International Journal of Technology and Applied Science

E-ISSN: 2230-9004   •   Impact Factor: 10.31

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 17 Issue 2 (February 2026) Submit your research before the last 3 days of this month to publish your research paper in the current issue.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 17 Issue 2
February 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Multilingual Row Detection in Tables: Beyond TATR with YOLO, Faster R-CNN, and TEDS-S

Author(s) Ms. Pranita Suresh Harpale, Prof. M. P. Chaudhari
Country India
Abstract Multilingual table structure recognition remains a challenging problem in document image analysis due to variations in scripts, layouts, and formatting styles. Although transformer-based approaches such as Table Transformer (TATR) have achieved strong performance on English-centric benchmarks, their effectiveness on multilingual documents is still limited. This paper presents a comprehensive study on multilingual row detection in tables by extending beyond TATR and evaluating object detection–based models, including YOLO and Faster R-CNN. The models are assessed using the structure-aware TEDS-S metric to ensure a fair and meaningful comparison across diverse table layouts and languages. Experiments conducted on large-scale public datasets and multilingual table documents demonstrate that CNN-based detectors offer competitive performance in row localization, while transformer-based methods excel in capturing global structural relationships. The findings highlight key trade-offs between accuracy, robustness, and computational efficiency, and establish strong baselines for multilingual table row detection. This work contributes practical insights for designing scalable and language-agnostic table understanding systems.
Keywords Table Structure Recognition, Multilingual Documents, Table Row Detection, YOLO, Faster R-CNN, Table Transformer, TEDS-S, Document Image Analysis
Field Computer
Published In Volume 17, Issue 2, Array 2026
Published On 2026-02-14
Cite This Multilingual Row Detection in Tables: Beyond TATR with YOLO, Faster R-CNN, and TEDS-S - Ms. Pranita Suresh Harpale, Prof. M. P. Chaudhari - IJTAS Volume 17, Issue 2, Array 2026.
View / Download PDF File

Share this



CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJTAS DOI prefix is
10.71097/IJTAS

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 15 Isu 12Cover Page Vol 15 Isu 11Cover Page Vol 15 Isu 10

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJTAS
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us Message on WhatsApp +91-9687-182-185 editor@ijtas.com