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

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challenge. Contemporary OMR techniques, grounded in machine learning principles, have a critical requirement: a labeled dataset for training. This presents a practical challenge due to the extensive manual effort required, coupled with the fact that the availability of suitable data for creating tr

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achine translation, document information retrieval, and structured data extraction from documents. However, most publicly available datasets in the field of layout analysis primarily consist of documents with a single layout type, are in the English language, and are limited to PDF documents. In thi

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https://doi.org/10.1007/978-3-030-68375-7documents often contain large amounts of personal data, their usage can pose a threat to user privacy and weaken the bonds of trust between humans and AI services. In response to these concerns, legislation advocating “the right to be forgotten” has recently been proposed, allowing users to request

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e current work on zero-shot learning in document image classification remains scarce. The existing studies either focus exclusively on zero-shot inference, or their evaluation does not align with the established criteria of zero-shot evaluation in the visual recognition domain. We provide a comprehe

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s and tasks, including document-specific ones. On the other hand, there is a trend to train multi-modal transformer architectures tailored for document understanding that are designed specifically to fuse textual inputs with the corresponding document layout. This involves a separate fine-tuning ste

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https://doi.org/10.1007/BFb0048530teps, such as layout analysis and optical character recognition (OCR), for information extraction from document images. We attempt to provide some answers through experiments conducted on a new database of food labels. The goal is to extract nutritional values from cellphone pictures taken in grocer

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classification (DIC). While VRD research is dependent on increasingly sophisticated and cumbersome models, the field has neglected to study efficiency via model compression. Here, we design a KD experimentation methodology. for more lean, performant models on document understanding (DU) tasks that

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https://doi.org/10.1007/978-3-642-71896-0for many research tasks, including text recognition, but it is costly to annotate them. Therefore, methods utilizing unlabeled data are researched. We study self-supervised pre-training methods based on masked label prediction using three different approaches – Feature Quantization, VQ-VAE, and Post