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New EMBLEM method improves multi-script table detection using masking

By PulseAugur Editorial · [1 sources] ·

Researchers have developed EMBLEM, a novel masking-based approach to enhance multi-script table detection in documents. This method aims to improve the performance of models trained on English documents when applied to multilingual and multi-script texts. To support this, a new dataset called MANDALA has been created, featuring 2,323 pages across 18 languages and 15 scripts. Experiments show that EMBLEM significantly boosts table detection accuracy on the MANDALA dataset, achieving a substantial F1-score gain even without multi-script training data. AI

IMPACT Enhances document analysis capabilities for multilingual datasets, potentially improving information retrieval and data extraction from diverse sources.

RANK_REASON The cluster describes a new research paper detailing a novel method and dataset for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New EMBLEM method improves multi-script table detection using masking

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The cluster describes a new research paper detailing a novel method and dataset for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Dhruv Kudale, Udhay Brahmi, Ganesh Ramakrishnan ·

    EMBLEM: Enhancing Multi-script Table Detection through Masking

    arXiv:2609.08330v1 Announce Type: new Abstract: Table detection is a core task in document analysis, supporting downstream applications such as information retrieval, document reconstruction, and visual question answering. While existing deep learning models perform well on Engli…

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