Metadata-Version: 2.1
Name: python-doctr
Version: 0.1.1
Summary: Extract valuable text information from your documents
Home-page: https://github.com/mindee/doctr
Author: François-Guillaume Fernandez, Charles Gaillard
Author-email: fg@mindee.co
License: Apache
Download-URL: https://github.com/mindee/doctr/tags
Description: 
        # DocTR: Document Text Recognition
        
        [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE) ![Build Status](https://github.com/mindee/doctr/workflows/python-package/badge.svg) [![codecov](https://codecov.io/gh/mindee/doctr/branch/main/graph/badge.svg?token=577MO567NM)](https://codecov.io/gh/mindee/doctr) [![CodeFactor](https://www.codefactor.io/repository/github/mindee/doctr/badge?s=bae07db86bb079ce9d6542315b8c6e70fa708a7e)](https://www.codefactor.io/repository/github/mindee/doctr) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/340a76749b634586a498e1c0ab998f08)](https://app.codacy.com/gh/mindee/doctr?utm_source=github.com&utm_medium=referral&utm_content=mindee/doctr&utm_campaign=Badge_Grade) [![Doc Status](https://github.com/mindee/doctr/workflows/doc-status/badge.svg)](https://mindee.github.io/doctr) [![Pypi](https://img.shields.io/badge/pypi-v0.1.0-blue.svg)](https://pypi.org/project/python-doctr/) 
        
        Extract valuable information from your documents.
        
        
        
        ## Table of Contents
        
        * [Getting Started](#getting-started)
          * [Prerequisites](#prerequisites)
          * [Installation](#installation)
        * [Usage](#usage)
          * [Python package](#python-package)
          * [Docker container](#docker-container)
          * [Example script](#example-script)
          * [Demo app](#demo-app)
        * [Documentation](#documentation)
        * [Contributing](#contributing)
        * [License](#license)
        
        
        
        ## Getting started
        
        ### Prerequisites
        
        - Python 3.6 (or higher)
        - [pip](https://pip.pypa.io/en/stable/)
        
        ### Installation
        
        You can install the latest release of the package using [pypi](https://pypi.org/project/python-doctr/) as follows:
        
        ```shell
        pip install python-doctr
        ```
        
        Or you can install it from source:
        
        ```shell
        git clone https://github.com/mindee/doctr.git
        pip install -e doctr/.
        ```
        
        
        ## Usage
        
        ### Python package
        
        You can use the library like any other python package to analyze your documents as follows:
        
        ```python
        from doctr.documents import read_pdf, read_img
        from doctr.models import ocr_db_crnn_vgg
        
        model = ocr_db_crnn_vgg(pretrained=True)
        # PDF
        doc = read_pdf("path/to/your/doc.pdf")
        result = model([doc])
        # Image
        page = read_img("path/to/your/img.jpg")
        result = model([[page]])
        # Export
        json_output = result[0].export()
        ```
        
        For an exhaustive list of pretrained models available, please refer to the [documentation](https://mindee.github.io/doctr/models.html).
        
        ### Docker container
        
        If you are to deploy containerized environments, you can use the provided Dockerfile to build a docker image:
        
        ```shell
        docker build . -t <YOUR_IMAGE_TAG>
        ```
        
        ### Example script
        
        An example script is provided for a simple documentation analysis of a PDF file:
        
        ```shell
        python scripts/analyze.py path/to/your/doc.pdf
        ```
        All script arguments can be checked using `python scripts/analyze.py --help`
        
        ### Demo app
        
        A minimal demo app is provided for you to play with the text detection model!
        
        You will need an extra dependency ([Streamlit](https://streamlit.io/)) for the app to run:
        ```shell
        pip install -r demo/requirements.txt
        ```
        You can then easily run your app in your default browser by running:
        
        ```shell
        streamlit run demo/app.py
        ```
        
        
        ## Documentation
        
        The full package documentation is available [here](https://mindee.github.io/doctr/) for detailed specifications. The documentation was built with [Sphinx](https://www.sphinx-doc.org/) using a [theme](github.com/readthedocs/sphinx_rtd_theme) provided by [Read the Docs](https://readthedocs.org/).
        
        
        
        ## Contributing
        
        Please refer to `CONTRIBUTING` if you wish to contribute to this project.
        
        
        
        ## License
        
        Distributed under the Apache 2.0 License. See `LICENSE` for more information.
Keywords: ocr,deep learning,tensorflow,text detection,text recognition
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
