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README.md
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TABME++ replaces the previous OCR with commericial-quality OCR obtained through Microsoft's OCR services.
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- **Curated by:** Roots Automation
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- **Language(s) (NLP):** English
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- **License:** MIT
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## Uses
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<!-- Address questions around how the dataset is intended to be used. -->
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### Direct Use
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
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[More Information Needed]
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## Dataset Structure
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## Dataset Creation
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TABME++ replaces the previous OCR with commericial-quality OCR obtained through Microsoft's OCR services.
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- **Curated by:** UCSF, UCL, University of Cambridge, Vector.ai, Roots Automation
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- **Language(s) (NLP):** English
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- **License:** MIT
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## Uses
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### Direct Use
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This dataset is intended to be used for page stream segmentation: the segmentation of a stream of ordered pages into coherent atomic documents.
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## Dataset Structure
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Each row of the dataset corresponds to one page of one document.
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Each page has the following features:
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- `doc_id`, str: The unique document id this page belongs to
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- `pg_id`, int: The page id within its document
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- `ocr`, str: A string containing the OCR annotations from Microsoft OCR. These can be loaded as a Python dictionary with `json.loads` (or equivalent).
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- `img`, binary: The raw bytes of the page image. These can be converted back to a PIL.Image with `Image.open(io.BytesIO(bytes))` (or equivalent).
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This dataset is given such that each document appears once.
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To build out the full aggregated synthetic streams, one needs to collate the unique documents according to the streams described in the [streams sub-folder](streams/).
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## Dataset Creation
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