VOCR for Video Optical Character Recognition.

This module allows you to extract data from an image, a video, or your screen directly.

The module is mainly based on OpenCV and EasyOCR technologies.

python3 vocr.py -h

usage: vocr.py [-h] -d DFILE [-i IFILE] [-v VFILE] [-o OFILE] [-r RFILE]

options:
  -h, --help            show this help message and exit
  -d DFILE, --dfile DFILE
                        path to input data
  -i IFILE, --ifile IFILE
                        path to image file
  -v VFILE, --vfile VFILE
                        path to video file
  -o OFILE, --ofile OFILE
                        path to optionaly output image/video file
  -r RFILE, --rfile RFILE
                        path to optionaly result file

You can reproduce the examples directly by typing the following commands:

python3 vocr.py -d example/data.json -i example/image.png -o example/output/image.png

python3 vocr.py -d example/data.json -v example/video.mp4 -o example/output/video.avi -r example/output/result.json

No examples are provided for the stream but you can create your own. To do so, simply leave the -i and -v options blank.

There are two possible uses :

• Directly from the terminal (explained in detail below)
• By integrating the folder into your project :

pip install git+https://github.com/kiitoss/vocr.git

import vocr
import json

# Called at every scan
def callback(dict):
    print('callback:', dict)


def main():
    # Extract data from stream, call `callback` after every scan
    # Stop only when user press CTRL + C
    data = vocr.extract_data('data.json', callback=callback)

    # Print the final data when user pressed CTRL+C
    print(json.dumps(data, indent=4))


if __name__ == "__main__":
    main()

• Use the -i option to scan an image
• Use the -v option to scan a video

If none of the options are selected, then the main screen will be used as the video stream.

You can specify the -r option to define a result file.

If the data comes from the screen as a video stream, then you will need to specify this option.

The terminal will be used to display the data in real time.

You can specify the -o option to define an output file.

If you process an image, the output file will be the same image annotated with the extracted information.

If it is a video or the video stream from a screen, then the output file will be a video including the extracted information.

The input data file is a JSON which contains a list of objects with a label, a box and potentially a 'match-pattern' attribute.

[
  {
    "label": "center",
    "box": [600, 440, 700, 200]
  },
  {
    "label": "top.left",
    "box": [180, 70, 250, 250],
    "match-pattern": {
      "circle": "example/patterns/circle.png",
      "square": "example/patterns/square.png"
    }
  },
  {
    "label": "bottom.right",
    "box": [1370, 720, 300, 300],
    "match-pattern": {
      "circle": "example/patterns/circle.png",
      "square": "example/patterns/square.png"
    }
  }
]

The output data file is another JSON that could look like this:

[
  {
    "id": 1,
    "progress": "1 / 68 ~ 0:0:2",
    "data": {
      "center": [],
      "top.left": null,
      "bottom.right": null
    }
  },
  {
    "id": 2,
    "progress": "11 / 68 ~ 0:0:4",
    "data": {
      "center": ["Paris"],
      "top.left": null,
      "bottom.right": null
    }
  },
  {
    "id": 7,
    "progress": "61 / 68 ~ 0:0:0",
    "data": {
      "center": ["Prague"],
      "top.left": "circle",
      "bottom.right": null
    }
  }
]

python3 vocr.py -i <path-to-image> -d data.json

python3 vocr.py -i <path-to-image> -d data.json -r result.json

python3 vocr.py -i <path-to-image> -d data.json -o output.png

python3 vocr.py -i <path-to-image> -d data.json -o output.png -r result.json

python3 vocr.py -v <path-to-video> -d data.json -r result.json

Here, the frame progression will be shown in the terminal :

Frame 100/200 (50%)

python3 vocr.py -v <path-to-video> -d data.json -r result.json -o output.avi

python3 vocr.py -d data.json -r result.json

Here the data will be shown on the terminal, depending on what you have included in the data file.

python3 vocr.py -v <path-to-video> -d data.json -r result.json -o output.avi

Enjoy :)