Titlebook: Document Analysis and Recognition – ICDAR 2024 Workshops; Athens, Greece, Augu Harold Mouchère,Anna Zhu Conference proceedings 2024 The Edi

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s on the same data, we find that the CNN-based Kraken model slightly outperforms the transformer-based TESTR model on recognition character accuracy and some object-level metrics, even though it lags behind on pixel-level metrics.

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An Interpretable Deep Learning Approach for?Morphological Script Type Analysis prototypes, representative of letter morphology, and provide qualitative and quantitative tools for their comparison and analysis. We demonstrate our approach by applying it to the . script type and its two subtypes formalized by A. Derolez: Northern and Southern ..

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MONSTERMASH: Multidirectional, Overlapping, Nested, Spiral Text Extraction for?Recognition Models ofs on the same data, we find that the CNN-based Kraken model slightly outperforms the transformer-based TESTR model on recognition character accuracy and some object-level metrics, even though it lags behind on pixel-level metrics.

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Ablation Study of a Multimodal Gat Network on Perfect Synthetic and Real-world Data to Investigate tthetic and an imperfect real-world dataset. The results of the study show the importance of language modules for semantic embeddings in multimodal invoice recognition and illustrate the impact of data annotation quality. We further contribute an adapted GAT model for German invoices.

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