Metadata-Version: 2.1
Name: deeptext
Version: 0.1.3
Summary: A cross-platform framework for deep learning based text detection, recoginition and parsing
Home-page: https://github.com/fcakyon/deeptext
Author: Fatih Cagatay Akyon
License: MIT
Description: [![PyPI version](https://badge.fury.io/py/deeptext.svg)](https://badge.fury.io/py/deeptext)
        [![Conda version](https://anaconda.org/fcakyon/deeptext/badges/version.svg)](https://anaconda.org/fcakyon/deeptext)
        [![CI](https://github.com/fcakyon/deeptext/workflows/CI/badge.svg)](https://github.com/fcakyon/deeptext/actions?query=event%3Apush+branch%3Amaster+is%3Acompleted+workflow%3ACI)
        
        # deeptext
        A cross-platform framework for deep learning based text detection, recoginition and parsing
        
        
        ## Getting started
        ### Installation
        - Install using conda for Linux, Mac and Windows (preferred):
        ```console
        conda install -c fcakyon deeptext
        ```
        - Install using pip for Linux and Mac:
        ```console
        pip install deeptext
        ```
        Install [teserract-ocr](https://tesseract-ocr.github.io/tessdoc/Home.html) for text recognition.
        
        ### Basic Usage
        ```python
        # import package
        import deeptext
        
        # set image path and export folder directory
        image_path = 'idcard.png'
        output_dir = 'outputs/'
        
        # apply text detection and export detected regions to output directory
        detection_result = deeptext.detect_text(image_path, output_dir)
        
        # apply text recognition to detected texts
        recognition_result = deeptext.recognize_text(image_path=detection_result["text_crop_paths"])
        ```
        
        ### Advanced Usage
        You can pass filter parameters if you want to scrap texts from image by predefined regions.
        ```python
        # import package
        import deeptext
        
        # set image path and export folder directory
        image_path = 'idcard.png'
        output_dir = 'outputs/'
        
        # define regions that you want to scrap, by quad (box) points
        filter_params = {"type": "box"
                         "boxes": [[[0.1460 , 0.0395],
                                    [0.8417, 0.0535],
                                    [0.8412, 0.1099],
                                    [0.1455, 0.0959]],
                                   [[0.3467, 0.3398],
                                    [0.5417, 0.3535],
                                    [0.5412, 0.4099],
                                    [0.3455, 0.3959]]],
                         "marigin_x": 0.05,
                         "marigin_y": 0.05,
                         "min_intersection_ratio": 0.9}
        
        # or define regions that you want to scrap, by centroids
        filter_params = {"type":"centroid",
                         "centers": [[0.44, 0.49],[0.49, 0.08]],
                         "marigin_x": 0.03,
                         "marigin_y": 0.05}
        
        # apply craft text detection in predefined regions and export detected regions to output directory
        detection_result = deeptext.detect_text(image_path,
                                                 output_dir,
                                                 detector="craft",
                                                 filter_params=filter_params)
                                                 
        # apply tesseract (eng) text recognition to detected texts
        recognition_result = deeptext.recognize_text(image_path=detection_result["text_crop_paths"],
                                                     recognizer="tesseract-eng")
        ```
        
        ## Updates
        **6 April, 2020**: Conda package release
        
        **3 April, 2020**: Tesseract text recoginition and positional text scraping support
        
        **30 March, 2020**: Craft text detector support
        
        ## TODO
        - [X] Craft text detection (inference)
        - [ ] Ctpn text detection (inference)
        - [ ] Psenet text detection (inference)
        - [X] Tesseract text recoginition (inference)
        - [ ] Aster text recognition (training and inference)
        - [ ] Moran text recognition (training and inference)
        - [X] Positional text scraping
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.5
Description-Content-Type: text/markdown
