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        "author": "Konstantin Baierer, Kay-Michael W\u00fcrzner, Robert Sachunsky",
        "author_email": "unixprog@gmail.com, wuerzner@gmail.com, sachunsky@informatik.uni-leipzig.de",
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        "description": "# ocrd_tesserocr\n\n> Crop, deskew, segment into regions / tables / lines / words, or recognize with tesserocr\n\n[![image](https://circleci.com/gh/OCR-D/ocrd_tesserocr.svg?style=svg)](https://circleci.com/gh/OCR-D/ocrd_tesserocr)\n[![image](https://img.shields.io/pypi/v/ocrd_tesserocr.svg)](https://pypi.org/project/ocrd_tesserocr/)\n[![image](https://codecov.io/gh/OCR-D/ocrd_tesserocr/branch/master/graph/badge.svg)](https://codecov.io/gh/OCR-D/ocrd_tesserocr)\n[![Docker Automated build](https://img.shields.io/docker/automated/ocrd/tesserocr.svg)](https://hub.docker.com/r/ocrd/tesserocr/tags/)\n\n## Introduction\n\nThis package offers [OCR-D](https://ocr-d.de/en/spec) compliant [workspace processors](https://ocr-d.de/en/spec/cli) for (much of) the functionality of [Tesseract](https://github.com/tesseract-ocr) via its Python API wrapper [tesserocr](https://github.com/sirfz/tesserocr). (Each processor is a parameterizable step in a configurable [workflow](https://ocr-d.de/en/workflows) of the [OCR-D functional model](https://ocr-d.de/en/about). There are usually various alternative processor implementations for each step. Data is represented with [METS](https://ocr-d.de/en/spec/mets) and [PAGE](https://ocr-d.de/en/spec/page).)\n\nIt includes image preprocessing (cropping, binarization, deskewing), layout analysis (region, table, line, word segmentation), script identification, font style recognition and text recognition. \n\nMost processors can operate on different levels of the PAGE hierarchy, depending on the workflow configuration. In PAGE, image results are referenced (read and written) via `AlternativeImage`, text results via `TextEquiv`, font attributes via `TextStyle`, script via `@primaryScript`, deskewing via `@orientation`, cropping via `Border` and segmentation via `Region` / `TextLine` / `Word` elements with `Coords/@points`.\n\n## Installation\n\n### With docker\n\nThis is the best option if you want to run the software in a container.\n\nYou need to have [Docker](https://docs.docker.com/install/linux/docker-ce/ubuntu/)\n\n\n    docker pull ocrd/tesserocr\n\n\nTo run with docker:\n\n\n    docker run -v path/to/workspaces:/data ocrd/tesserocr ocrd-tesserocrd-crop ...\n\n\n### From PyPI and PPA\n\nThis is the best option if you want to use the stable, released version.\n\n---\n\n**NOTE**\n\nocrd_tesserocr requires **Tesseract >= 4.1.0**. The Tesseract packages\nbundled with **Ubuntu < 19.10** are too old. If you are on Ubuntu 18.04 LTS,\nplease use [Alexander Pozdnyakov's PPA](https://launchpad.net/~alex-p/+archive/ubuntu/tesseract-ocr) repository,\nwhich has up-to-date builds of Tesseract and its dependencies:\n\n```sh\nsudo add-apt-repository ppa:alex-p/tesseract-ocr\nsudo apt-get update\n```\n\n---\n\n```sh\nsudo apt-get install python3 python3-pip libtesseract-dev libleptonica-dev tesseract-ocr wget\npip install ocrd_tesserocr\n```\n\n### From git\n\nUse this option if you want to change the source code or install the latest, unpublished changes.\n\nWe strongly recommend to use [venv](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).\n\n```sh\ngit clone https://github.com/OCR-D/ocrd_tesserocr\ncd ocrd_tesserocr\n# install Tesseract:\nsudo make deps-ubuntu # or manually from git or via ocrd_all\n# install tesserocr and ocrd_tesserocr:\nmake deps        # or pip install -r requirements\nmake install     # or pip install .\n```\n\n## Models\n\nTesseract comes with synthetically trained models for languages (`tesseract-ocr-{eng,deu,frk,...}` or scripts (`tesseract-ocr-script-{latn,frak,...}`). In addition, various models [trained](https://github.com/tesseract-ocr/tesstrain) on scan data are available from the community.\n\nNote that since all OCR-D processors must resolve file/data resources in a [standardized way](https://ocr-d.de/en/spec/cli#processor-resources), `ocrd-tesserocr-recognize` expects the recognition models to be installed in `$XDG_DATA_HOME/ocrd-resources/ocrd-tesserocr-recognize` (where, usually, `$XDG_DATA_HOME=$HOME/.local/share`). This is the default **resource location** used by `ocrd resmgr`, which you can use to download and list models:\n\n    ocrd resmgr --help\n\n(However, for backwards compatibility, this can be overriden by defining `$TESSDATA_PREFIX` in the environment. In this case users must install models manually \u2013 by linking/copying or downloading them into that directory. The same is true for the non-default location used by the system packages `tesseract-ocr-*`, which is usually `/usr/share/tesseract-ocr/4.00/tessdata`.)\n\nCf. [OCR-D model guide](https://ocr-d.de/en/models).\n\nModels always use the filename suffix `.traineddata`, but are just loaded by their basename. You will need **at least** `eng` and `osd` (even for segmentation and deskewing), probably also `Latin` and `Fraktur` etc.\n\nAs of v0.13.1, you can configure `ocrd-tesserocr-recognize` to select models **dynamically** segment by segment, either via custom conditions on the PAGE-XML annotation (presented as XPath rules), or by automatically choosing the model with highest confidence.\n\n## Usage\n\nFor details, see docstrings in the individual processors and [ocrd-tool.json](ocrd_tesserocr/ocrd-tool.json) descriptions,\nor simply `--help`.\n\nAvailable [OCR-D processors](https://ocr-d.de/en/spec/cli) are:\n\n- [ocrd-tesserocr-crop](ocrd_tesserocr/crop.py) (simplistic)\n  - sets `Border` of pages and adds `AlternativeImage` files to the output fileGrp\n- [ocrd-tesserocr-deskew](ocrd_tesserocr/deskew.py) (for skew and orientation; mind `operation_level`)\n  - sets `@orientation` of regions or pages and adds `AlternativeImage` files to the output fileGrp\n- [ocrd-tesserocr-binarize](ocrd_tesserocr/binarize.py) (Otsu \u2013 not recommended)  \n  - adds `AlternativeImage` files to the output fileGrp\n- [ocrd-tesserocr-recognize](ocrd_tesserocr/recognize.py) (optionally including segmentation; mind `segmentation_level` and `textequiv_level`)\n  - adds `TextRegion`s, `TableRegion`s, `ImageRegion`s, `MathsRegion`s, `SeparatorRegion`s, `NoiseRegion`s, `ReadingOrder` and `AlternativeImage` to `Page` and sets their `@orientation` (optionally)\n  - adds `TextRegion`s to `TableRegion`s and sets their `@orientation` (optionally)\n  - adds `TextLine`s to `TextRegion`s (optionally)\n  - adds `Word`s to `TextLine`s (optionally)\n  - adds `Glyph`s to `Word`s (optionally)\n  - adds `TextEquiv`\n- [ocrd-tesserocr-segment](ocrd_tesserocr/segment.py) (all-in-one segmentation \u2013 recommended; delegates to `recognize`)  \n  - adds `TextRegion`s, `TableRegion`s, `ImageRegion`s, `MathsRegion`s, `SeparatorRegion`s, `NoiseRegion`s, `ReadingOrder` and `AlternativeImage` to `Page` and sets their `@orientation`\n  - adds `TextRegion`s to `TableRegion`s and sets their `@orientation`\n  - adds `TextLine`s to `TextRegion`s\n  - adds `Word`s to `TextLine`s\n  - adds `Glyph`s to `Word`s\n- [ocrd-tesserocr-segment-region](ocrd_tesserocr/segment_region.py) (only regions \u2013 with overlapping bboxes; delegates to `recognize`)\n  - adds `TextRegion`s, `TableRegion`s, `ImageRegion`s, `MathsRegion`s, `SeparatorRegion`s, `NoiseRegion`s and `ReadingOrder` to `Page` and sets their `@orientation`\n- [ocrd-tesserocr-segment-table](ocrd_tesserocr/segment_table.py) (only table cells; delegates to `recognize`)\n  - adds `TextRegion`s to `TableRegion`s\n- [ocrd-tesserocr-segment-line](ocrd_tesserocr/segment_line.py) (only lines \u2013 from overlapping regions; delegates to `recognize`)\n  - adds `TextLine`s to `TextRegion`s\n- [ocrd-tesserocr-segment-word](ocrd_tesserocr/segment_word.py) (only words; delegates to `recognize`)\n  - adds `Word`s to `TextLine`s\n- [ocrd-tesserocr-fontshape](ocrd_tesserocr/fontshape.py) (only text style \u2013 via Tesseract 3 models)\n  - adds `TextStyle` to `Word`s\n\nThe text region `@type`s detected are (from Tesseract's [PolyBlockType](https://github.com/tesseract-ocr/tesseract/blob/11297c983ec7f5c9765d7fa4faa48f5150cf2d38/include/tesseract/publictypes.h#L52-L69)):\n- `paragraph`: normal block (aligned with others in the column)\n- `floating`: unaligned block (`is in a cross-column pull-out region`)\n- `heading`: block that `spans more than one column`\n- `caption`: block for `text that belongs to an image`\n\nIf you are unhappy with these choices, consider post-processing with a dedicated custom processor in Python, or by modifying the PAGE files directly (e.g. `xmlstarlet ed --inplace -u '//pc:TextRegion/@type[.=\"floating\"]' -v paragraph filegrp/*.xml`).\n\nAll segmentation is currently done as **bounding boxes** only by default, i.e. without precise polygonal outlines. For dense page layouts this means that neighbouring regions and neighbouring text lines may overlap a lot. If this is a problem for your workflow, try post-processing like so:\n- after line segmentation: use `ocrd-cis-ocropy-resegment` for polygonalization, or `ocrd-cis-ocropy-clip` on the line level\n- after region segmentation: use `ocrd-segment-repair` with `plausibilize` (and `sanitize` after line segmentation)\n\nIt also means that Tesseract should be allowed to segment across multiple hierarchy levels at once, to avoid introducing inconsistent/duplicate text line assignments in text regions, or word assignments in text lines. Hence,\n- prefer `ocrd-tesserocr-recognize` with `segmentation_level=region` over `ocrd-tesserocr-segment` followed by `ocrd-tesserocr-recognize`, if you want to do all in one with Tesseract,\n- prefer `ocrd-tesserocr-recognize` with `segmentation_level=line` over `ocrd-tesserocr-segment-line` followed by `ocrd-tesserocr-recognize`, if you want to do everything but region segmentation with Tesseract,\n- prefer `ocrd-tesserocr-segment` over `ocrd-tesserocr-segment-region` followed by (`ocrd-tesserocr-segment-table` and) `ocrd-tesserocr-segment-line`, if you want to do everything but recognition with Tesseract.\n\nHowever, you can also run `ocrd-tesserocr-segment*` and `ocrd-tesserocr-recognize` with `shrink_polygons=True` to get **polygons** by post-processing each segment, shrinking to the convex hull of all its symbol outlines.\n\n## Testing\n\n```sh\nmake test\n```\n\nThis downloads some test data from https://github.com/OCR-D/assets under `repo/assets`, and runs some basic test of the Python API as well as the CLIs.\n\nSet `PYTEST_ARGS=\"-s --verbose\"` to see log output (`-s`) and individual test results (`--verbose`).\n\n\n",
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