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

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TrOCR Meets Language Models: An End-to-End Post-correction Approachances visual and linguistic information, preserving the authenticity of the original texts. Furthermore, the model is able to adapt to historical data even when the recogniser is trained solely on contemporary data, mitigating the need for a large number of annotated historical handwritten images.

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: Domain Adaptive Document Restoration with?a?Layer Separation Approach qualitatively and quantitatively using a new real-world dataset, ., developed for this study. Initially trained on a synthetically generated dataset, our model demonstrates strong generalization capabilities for the DIR task, offering a promising solution for handling variability in real-world data. Our code is accessible on this GitHub(.).

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Investigating Neural Networks and?Transformer Models for?Enhanced Comic Decoding (eBDtheque, DCM772, Manga109) and using different metrics (Precision, Recall, Average Precision), we conclude that pre-trained self-supervised transformer models can competently outperform state of the art approaches, which often require further fine-tuning to achieve comparable results.

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Normalized vs Diplomatic Annotation: A Case Study of?Automatic Information Extraction from?Handwritte than?15 different writers) but with different annotation methods.?Our findings indicate that normalized annotation is more effective?for fields that can be standardized, such as dates and places of birth, whereas diplomatic annotation performs much better for fields containing names and surnames, which can not be standardized.

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ion process is provided, in order to render the materials in question machine-readable, while in the second part the potential for linguistic research is highlighted, through a case-study exploring aspects of the ‘Greek language question’, as discussed in the parliamentary context, within the wider framework of language policy making.

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Peter C. Maloney,E. R. Kashket,T. H. Wilsonon including character’s appearance, posture, mood, dialogues etc. We believe that such enriched content description can be easily used to produce audiobook and eBook with various voices for characters, captions and playing sound effects.