Problem
Research questions and friction points this paper is trying to address.
Multi-script Table Detection
script diversity
labeled data
EMBLEM: Enhancing Multi-script Table Detection through Masking
🤖 AI Summary
为解决多语言文档中表格检测问题，通过引入新数据集MANDALA和提出基于掩码的方法EMBLEM来增强模型对不同文字布局的识别能力。
📝 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 English and Chinese documents, they struggle with multilingual, multi-script documents due to script diversity and the limited availability of labeled data. To address this challenge, we introduce MANDALA (Multi-script Annotated Documents for Table Detection), a manually curated dataset of 2,323 table-containing pages spanning 18 languages and 15 scripts across diverse domains. We also propose EMBLEM, a masking-based paradigm for Multi-script Table Detection (MTD). EMBLEM generates masked images that conceal script- and font-specific details, enabling models pre-trained on abundant English documents to focus on script-agnostic page layout. Experiments across three table detection architectures show that EMBLEM consistently outperforms strong baselines on MANDALA while remaining competitive on five standard English-dominant benchmarks. Using only English masked images for fine-tuning, with no multi-script training data, EMBLEM achieves an absolute F1-score gain of 20.8% on MANDALA. We release MANDALA along with the accompanying code and models at https://github.com/IITB-LEAP-OCR/EMBLEM.git.
Innovation
Methods, ideas, or system contributions that make the work stand out.
masking-based paradigm
Multi-script Table Detection
script-agnostic page layout
pre-trained models
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