Packaged, Pytorch-based, easy to use, cross-platform version of the CRAFT text detector

Related tags

Computer Visionpythonwindowsmacoslinuxworkflowocrcomputer-visiondeep-learningneural-networkanacondatextcraftpypiactionspytorchvisiondocumenttext-detection
Overview

CRAFT: Character-Region Awareness For Text detection

Downloads PyPI version Conda version CI

Packaged, Pytorch-based, easy to use, cross-platform version of the CRAFT text detector | Paper |

Overview

PyTorch implementation for CRAFT text detector that effectively detect text area by exploring each character region and affinity between characters. The bounding box of texts are obtained by simply finding minimum bounding rectangles on binary map after thresholding character region and affinity scores.

teaser

Getting started

Installation

  • Install using conda for Linux, Mac and Windows (preferred):
conda install -c fcakyon craft-text-detector
  • Install using pip for Linux and Mac:
pip install craft-text-detector

Basic Usage

# import Craft class
from craft_text_detector import Craft

# set image path and export folder directory
image_path = 'figures/idcard.png'
output_dir = 'outputs/'

# create a craft instance
craft = Craft(output_dir=output_dir, crop_type="poly", cuda=False)

# apply craft text detection and export detected regions to output directory
prediction_result = craft.detect_text(image_path)

# unload models from ram/gpu
craft.unload_craftnet_model()
craft.unload_refinenet_model()

Advanced Usage

# import craft functions
from craft_text_detector import (
    read_image,
    load_craftnet_model,
    load_refinenet_model,
    get_prediction,
    export_detected_regions,
    export_extra_results,
    empty_cuda_cache
)

# set image path and export folder directory
image_path = 'figures/idcard.png'
output_dir = 'outputs/'

# read image
image = read_image(image_path)

# load models
refine_net = load_refinenet_model(cuda=True)
craft_net = load_craftnet_model(cuda=True)

# perform prediction
prediction_result = get_prediction(
    image=image,
    craft_net=craft_net,
    refine_net=refine_net,
    text_threshold=0.7,
    link_threshold=0.4,
    low_text=0.4,
    cuda=True,
    long_size=1280
)

# export detected text regions
exported_file_paths = export_detected_regions(
    image_path=image_path,
    image=image,
    regions=prediction_result["boxes"],
    output_dir=output_dir,
    rectify=True
)

# export heatmap, detection points, box visualization
export_extra_results(
    image_path=image_path,
    image=image,
    regions=prediction_result["boxes"],
    heatmaps=prediction_result["heatmaps"],
    output_dir=output_dir
)

# unload models from gpu
empty_cuda_cache()
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Comments
  • Add more options for detect_text method

    Add more options for detect_text method

    Hi, sometime I don't want detect_text from file, I want detect_text directly from image in ndarray format, that will save more cost of I/O time. So I contribute this. Thanks for your work

    opened by ducviet00 2
  • Enable package to load model from local path

    Enable package to load model from local path

    When using the pypi package it should be allowed to use a model from a local path, because loading it from a remote location removes the control over what model is currently used. And might also result in pull limits being reached.

    enhancement 
    opened by TanjaBayer 1
  • Fix #8 - Fixing cuda issues in basic usage text detection

    Fix #8 - Fixing cuda issues in basic usage text detection

    Fixing issue #8

    In this quick-fix I referenced craft_net as a global variable. If this is not an acceptable workaround, then consider reorganizing the structure of the code.

    Have a nice day :)

    opened by gaborpelesz 1
  • accept customized weights path when loading models

    accept customized weights path when loading models

    path for the weight file can be specified by:

    load_craftnet_model(weight_path="path/to/weight")

    load_refinenet_model(weight_path="path/to/weight")
    opened by fcakyon 0
Releases(0.4.3)

  • 0.4.3(May 9, 2022)

    What's Changed

    • Enable package to load model from local path by @TanjaBayer in https://github.com/fcakyon/craft-text-detector/pull/53

    New Contributors

    • @TanjaBayer made their first contribution in https://github.com/fcakyon/craft-text-detector/pull/53

    Full Changelog: https://github.com/fcakyon/craft-text-detector/compare/0.4.2...0.4.3

    Source code(tar.gz)
    Source code(zip)

  • 0.4.2(Jan 6, 2022)

    What's Changed

    • fix opencv version by @fcakyon in https://github.com/fcakyon/craft-text-detector/pull/48

    Full Changelog: https://github.com/fcakyon/craft-text-detector/compare/0.4.1...0.4.2

    Source code(tar.gz)
    Source code(zip)

  • 0.4.1(Dec 20, 2021)

    What's Changed

    • fix crop export by @fcakyon in https://github.com/fcakyon/craft-text-detector/pull/45

    Full Changelog: https://github.com/fcakyon/craft-text-detector/compare/0.4.0...0.4.1

    Source code(tar.gz)
    Source code(zip)

  • 0.4.0(Jul 30, 2021)

  • 0.3.5(May 12, 2021)

  • 0.3.4(Apr 7, 2021)

    • add support for PIL and numpy images in addition to filepath. https://github.com/fcakyon/craft-text-detector/pull/28
    from PIL import Image
import numpy

# can be filepath, PIL image or numpy array
image = 'figures/idcard.png' 
image = Image.open("figures/idcard.png")
image = numpy.array(Image.open("figures/idcard.png"))

# apply craft text detection
prediction_result = craft.detect_text(image)
    Source code(tar.gz)
    Source code(zip)

  • 0.3.3(Mar 2, 2021)

  • 0.3.2(Mar 2, 2021)

    path for the weight file can be specified by:

    load_craftnet_model(weight_path="path/to/weight")

    load_refinenet_model(weight_path="path/to/weight")
    Source code(tar.gz)
    Source code(zip)

  • v0.3.1(May 14, 2020)

  • v0.3.0(May 14, 2020)

    • updated basic usage for better device handling, now Craft instance should be created before calling detect_text:
    # import Craft class
from craft_text_detector import Craft

# set image path and export folder directory
image_path = 'figures/idcard.png'
output_dir = 'outputs/'

# create a craft instance
craft = Craft(output_dir=output_dir, crop_type="poly", cuda=False)

# apply craft text detection and export detected regions to output directory
prediction_result = craft.detect_text(image_path)

# unload models from ram/gpu
craft.unload_craftnet_model()
craft.unload_refinenet_model()
    • some internal naming and styling changes
    Source code(tar.gz)
    Source code(zip)

  • v0.2.1(May 10, 2020)

  • v0.2.0a(Apr 22, 2020)

  • v0.2.0(Apr 22, 2020)

Owner
Senior Machine Learning Engineer, METU & Bilkent alum.
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