Metadata-Version: 2.1
Name: ocrd_calamari
Version: 1.0.1
Summary: Calamari bindings
Home-page: https://github.com/OCR-D/ocrd_calamari
Author: Konstantin Baierer, Mike Gerber
Author-email: unixprog@gmail.com, mike.gerber@sbb.spk-berlin.de
License: Apache License 2.0
Description: # ocrd_calamari
        
        > Recognize text using [Calamari OCR](https://github.com/Calamari-OCR/calamari).
        
        [![image](https://circleci.com/gh/OCR-D/ocrd_calamari.svg?style=svg)](https://circleci.com/gh/OCR-D/ocrd_calamari)
        [![image](https://img.shields.io/pypi/v/ocrd_calamari.svg)](https://pypi.org/project/ocrd_calamari/)
        [![image](https://codecov.io/gh/OCR-D/ocrd_calamari/branch/master/graph/badge.svg)](https://codecov.io/gh/OCR-D/ocrd_calamari)
        
        ## Introduction
        
        **ocrd_calamari** offers a [OCR-D](https://ocr-d.de) compliant workspace processor for the functionality of Calamari OCR. It uses OCR-D workspaces (METS) with [PAGE XML](https://github.com/PRImA-Research-Lab/PAGE-XML) documents as input and output.
        
        This processor only operates on the text line level and so needs a line segmentation (and by extension a binarized 
        image) as its input.
        
        In addition to the line text it may also output word and glyph segmentation
        including per-glyph confidence values and per-glyph alternative predictions as
        provided by the Calamari OCR engine, using a `textequiv_level` of `word` or
        `glyph`. Note that while Calamari does not provide word segmentation, this
        processor produces word segmentation inferred from text
        segmentation and the glyph positions. The provided glyph and word segmentation
        can be used for text extraction and highlighting, but is probably not useful for
        further image-based processing.
        
        ![Example output as viewed in PAGE Viewer](https://github.com/OCR-D/ocrd_calamari/raw/screenshots/output-in-page-viewer.jpg)
        
        ## Installation
        
        ### From PyPI
        
        ```
        pip install ocrd_calamari
        ```
        
        ### From Repo
        
        ```sh
        pip install .
        ```
        
        ## Install models
        
        Download models trained on GT4HistOCR data:
        
        ```
        make gt4histocr-calamari1
        ls gt4histocr-calamari1
        ```
        
        Manual download: [model.tar.xz](https://qurator-data.de/calamari-models/GT4HistOCR/2019-12-11T11_10+0100/model.tar.xz)
        
        ## Example Usage
        Before using `ocrd-calamari-recognize` get some example data and model, and
        prepare the document for OCR:
        ```
        # Download model and example data
        make gt4histocr-calamari1
        make actevedef_718448162
        
        # Create binarized images and line segmentation using other OCR-D projects
        cd actevedef_718448162
        ocrd-olena-binarize -P impl sauvola-ms-split -I OCR-D-IMG -O OCR-D-IMG-BIN
        ocrd-tesserocr-segment-region -I OCR-D-IMG-BIN -O OCR-D-SEG-REGION
        ocrd-tesserocr-segment-line -I OCR-D-SEG-REGION -O OCR-D-SEG-LINE
        ```
        
        Finally recognize the text using ocrd_calamari and the downloaded model:
        ```
        ocrd-calamari-recognize -P checkpoint "../gt4histocr-calamari1/*.ckpt.json" -I OCR-D-SEG-LINE -O OCR-D-OCR-CALAMARI
        ```
        
        or
        
        ```
        ocrd-calamari-recognize -P checkpoint_dir "../gt4histocr-calamari1" -I OCR-D-SEG-LINE -O OCR-D-OCR-CALAMARI
        ```
        
        
        You may want to have a look at the [ocrd-tool.json](ocrd_calamari/ocrd-tool.json) descriptions
        for additional parameters and default values.
        
        ## Development & Testing
        For information regarding development and testing, please see
        [README-DEV.md](README-DEV.md).
        
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