Papers › VisualWordGrid: Information Extraction From Scanned Documents Using A Multimodal Approach
VisualWordGrid: Information Extraction From Scanned Documents Using A Multimodal Approach
Mohamed Kerroumi, Othmane Sayem, Aymen Shabou
We introduce a novel approach for scanned document representation to perform field extraction. It allows the simultaneous encoding of the textual, visual and layout information in a 3-axis tensor used as an input to a segmentation model. We improve the recent Chargrid and Wordgrid \cite{chargrid} models in several ways, first by taking into account the visual modality, then by boosting its robustness in regards to small datasets while keeping the inference time low. Our approach is tested on public and private document-image datasets, showing higher performances compared to the recent state-of-the-art methods.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Document Layout Analysis | RVL-CDIP | VisualWordGrid | FAR | 28.7 | #1 of 1 | Archive leaderboard | report |
| Document Layout Analysis | RVL-CDIP | VisualWordGrid | WAR | 18.7 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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