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Seven Segment Optical Character Recognition

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SSOCR

Seven Segment Optical Character Recognition

Algorithm

solution

DIGITS_LOOKUP = {
    (1, 1, 1, 1, 1, 1, 0): 0,
    (1, 1, 0, 0, 0, 0, 0): 1,
    (1, 0, 1, 1, 0, 1, 1): 2,
    (1, 1, 1, 0, 0, 1, 1): 3,
    (1, 1, 0, 0, 1, 0, 1): 4,
    (0, 1, 1, 0, 1, 1, 1): 5,
    (0, 1, 1, 1, 1, 1, 1): 6,
    (1, 1, 0, 0, 0, 1, 0): 7,
    (1, 1, 1, 1, 1, 1, 1): 8,
    (1, 1, 0, 0, 1, 1, 1): 9,
    (0, 0, 0, 0, 0, 1, 1): '-'
}

Digital recognition of seven-segment digital tube is relatively simple compared to handwritten numeral.

Detect the existence of the corresponding bit, then encode the image, you can accurately identify the number.

Requirements

  • opencv
  • numpy

Setup

git clone https://github.com/JeremyDemers/SSOCR.git
python ssocr.py images/test1.bmp -s

Results

test1.bmp res1.bmp test2.bmp res2.bmp

$ python ssocr.py -i images/test1.bmp
['-', 3, 0, '.', 3, 7]
$ python ssocr.py -i images/test2.bmp -s
[1, 7, 7, '.', 7]
$ python ssocr.py -i images/test3.bmp -s
[0, 7, 8, '.', 3]
$ python ssocr.py -i images/test4.bmp -s
[0, 7, 2, '.', 6]

Acknowledge

SSOCR

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  • Python 100.0%