Skip to content

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

License

Notifications You must be signed in to change notification settings

benscott/craft-text-detector

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

43 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

CRAFT: Character-Region Awareness For Text detection

downloads downloads fcakyon twitter
Build status PyPI version License: MIT

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 pip:
pip install craft-text-detector

Basic Usage

# import Craft class
from craft_text_detector import Craft

# set image path and export folder directory
image = 'figures/idcard.png' # can be filepath, PIL image or numpy array
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)

# 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 = 'figures/idcard.png' # can be filepath, PIL image or numpy array
output_dir = 'outputs/'

# read image
image = read_image(image)

# 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=image,
    regions=prediction_result["boxes"],
    output_dir=output_dir,
    rectify=True
)

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

# unload models from gpu
empty_cuda_cache()

About

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

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%