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FastTab model uses recursive module and 1D Transformers for table recognition

By PulseAugur Editorial · [2 sources] ·

Researchers have developed FastTab, a novel model for table structure recognition that utilizes a recursive module and 1D Transformers. This approach bypasses traditional autoregressive decoding by focusing on grid-centric reasoning. FastTab demonstrates competitive performance and low-latency inference across multiple benchmarks, with potential applications for camera-captured documents. AI

IMPACT Introduces a novel architecture for table structure recognition, potentially improving efficiency and accuracy in document analysis.

RANK_REASON The cluster contains an academic paper detailing a new model and its performance on benchmarks.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

FastTab model uses recursive module and 1D Transformers for table recognition

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The cluster contains an academic paper detailing a new model and its performance on benchmarks.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Thierry Paquet ·

    FastTab: A Fast Table Recognizer with a Tiny Recursive Module and 1D Transformers

    Table structure recognition (TSR) requires both table-level coherence (row/column counts, headers, spanning cells) and precise separator localization. We introduce FastTab, a grid-centric TSR model that avoids autoregressive HTML decoding by combining (i) a lightweight Tiny Recur…

  2. arXiv cs.CV TIER_1 English(EN) · Laziz Hamdi, Amine Tamasna, Pascal Boisson, Thierry Paquet ·

    FastTab: A Fast Table Recognizer with a Tiny Recursive Module and 1D Transformers

    arXiv:2605.22422v1 Announce Type: new Abstract: Table structure recognition (TSR) requires both table-level coherence (row/column counts, headers, spanning cells) and precise separator localization. We introduce FastTab, a grid-centric TSR model that avoids autoregressive HTML de…

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