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ADOPD is a large-scale dataset for document page decomposition, distinguished by a novel data-driven document taxonomy discovery method for data collection. This approach combines large-scale pretrained models with a human-in-the-loop process to ensure diversity and balance in the data. ADOPD includes densely annotated labels for document images, covering four tasks: Doc2Mask, Doc2Box, Doc2Tag, and Doc2Seq. Annotations for each image include human-labeled entity masks, text bounding boxes, and automatically generated tags and captions. Detailed experimental analyses validate the data-driven document taxonomy method and evaluate the four tasks using different models. ADOPD aims to support future research in document image understanding.
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# Dataset Information
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The ADOPD dataset contains a total of 120,000 images, with the following language distribution:
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- English: 60,000
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ADOPD is a large-scale dataset for document page decomposition, distinguished by a novel data-driven document taxonomy discovery method for data collection. This approach combines large-scale pretrained models with a human-in-the-loop process to ensure diversity and balance in the data. ADOPD includes densely annotated labels for document images, covering four tasks: Doc2Mask, Doc2Box, Doc2Tag, and Doc2Seq. Annotations for each image include human-labeled entity masks, text bounding boxes, and automatically generated tags and captions. Detailed experimental analyses validate the data-driven document taxonomy method and evaluate the four tasks using different models. ADOPD aims to support future research in document image understanding.
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# Dataset Information
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The ADOPD dataset contains a total of 120,000 images, with the following language distribution:
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- English: 60,000
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