Titlebook: Document Analysis and Recognition - ICDAR 2024; 18th International C Elisa H. Barney Smith,Marcus Liwicki,Liangrui Peng Conference proceedi

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Progressive Evolution from?Single-Point to?Polygon for?Scene Texte attained 86% of the accuracy relative to training with ground truth (GT); 3) Additionally, the proposed Point2Polygon can be seamlessly integrated to empower single-point spotters to generate polygons. This integration led to an impressive 82.5% accuracy for the generated polygons. It is worth men

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More and?Less: Enhancing Abundance and?Refining Redundancy for?Text-Prior-Guided Scene Text Image Suequired in the reconstruction of LR text images, which can well resolve information loss and generate more accurate super-resolution text images. Our proposed method consistently outperforms baselines employing text recognizers ASTER, MORAN, and CRNN by 1–2. on TextZoom, and achieves impressive gain

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GARDEN: Generative Prior Guided Network for?Scene Text Image Super-Resolutionn, leading to more efficient learning of both texture generation and text recovery. In addition, GARDEN introduces multi-scale sequential residual block (MS-SRB), a simple, efficient, and flexible structure for achieving the maximal utilization of generative priors. By leveraging enriched generative

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