https://github.com/fcakyon/craft-text-detector

Packaged, Pytorch-based, easy to use, cross-platform version of the CRAFT text detector
https://github.com/fcakyon/craft-text-detector

actions anaconda computer-vision craft deep-learning document hacktoberfest linux macos neural-network ocr pypi python pytorch text text-detection vision windows workflow

Last synced: 3 months ago
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Packaged, Pytorch-based, easy to use, cross-platform version of the CRAFT text detector

• Host: GitHub
• URL: https://github.com/fcakyon/craft-text-detector
• Owner: fcakyon
• License: mit
• Archived: true
• Created: 2020-04-14T10:52:26.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2022-05-09T10:55:28.000Z (over 2 years ago)
• Last Synced: 2024-08-02T11:15:29.256Z (6 months ago)
• Topics: actions, anaconda, computer-vision, craft, deep-learning, document, hacktoberfest, linux, macos, neural-network, ocr, pypi, python, pytorch, text, text-detection, vision, windows, workflow
• Language: Python
• Homepage:
• Size: 1.48 MB
• Stars: 249
• Watchers: 7
• Forks: 92
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# CRAFT: Character-Region Awareness For Text detection




















Packaged, Pytorch-based, easy to use, cross-platform version of the CRAFT text detector | [Paper](https://arxiv.org/abs/1904.01941) |

## 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.



## Getting started

### Installation

- Install using pip:

```console

pip install craft-text-detector

```

### Basic Usage

```python

# 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

```python

# 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()

```