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

Author

Listed:
  • Mahmoud SalahEldin Kasem

    (Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
    Multimedia Department, Faculty of Computers and Information, Assiut University, Assiut 71526, Egypt)

  • Mohamed Mahmoud

    (Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
    Information Technology Department, Faculty of Computers and Information, Assiut University, Assiut 71526, Egypt)

  • 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
    Information Technology Department, Faculty of Computers and Information, Assiut University, Assiut 71526, Egypt)

  • 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)

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.

Suggested Citation

  • Mahmoud SalahEldin Kasem & Mohamed Mahmoud & Bilel Yagoub & Mostafa Farouk Senussi & Mahmoud Abdalla & Hyun-Soo Kang, 2025. "HTTD: A Hierarchical Transformer for Accurate Table Detection in Document Images," Mathematics, MDPI, vol. 13(2), pages 1-20, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:2:p:266-:d:1567621
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    References listed on IDEAS

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    1. Nikos Kanellos & Marina C. Terzi & Nikolaos T. Giannakopoulos & Panagiotis Karountzos & Damianos P. Sakas, 2024. "The Economic Dynamics of Desktop and Mobile Customer Analytics in Advancing Digital Branding Strategies: Insights from the Agri-Food Industry," Sustainability, MDPI, vol. 16(14), pages 1-28, July.
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