HTTD: A Hierarchical Transformer for Accurate Table Detection in Document Images

Mahmoud SalahEldin Kasem, Mohamed Mahmoud, Bilel Yagoub, Mostafa Farouk Senussi, Mahmoud Abdalla and Hyun-Soo Kang ()
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Mahmoud SalahEldin Kasem: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
Mohamed Mahmoud: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
Bilel Yagoub: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
Mostafa Farouk Senussi: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
Mahmoud Abdalla: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
Hyun-Soo Kang: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea

Mathematics, 2025, vol. 13, issue 2, 1-20

Abstract: Table detection in document images is a challenging problem due to diverse layouts, irregular structures, and embedded graphical elements. In this study, we present HTTD (Hierarchical Transformer for Table Detection), a cutting-edge model that combines a Swin-L Transformer backbone with advanced Transformer-based mechanisms to achieve superior performance. HTTD addresses three key challenges: handling diverse document layouts, including historical and modern structures; improving computational efficiency and training convergence; and demonstrating adaptability to non-standard tasks like medical imaging and receipt key detection. Evaluated on benchmark datasets, HTTD achieves state-of-the-art results, with precision rates of 96.98% on ICDAR-2019 cTDaR, 96.43% on TNCR, and 93.14% on TabRecSet. These results validate its effectiveness and efficiency, paving the way for advanced document analysis and data digitization tasks.

Keywords: table detection; vision transformer; document processing; multiscale feature extraction; deformable attention; document image analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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