pip install all the needed packagesThe scanner takes a single pdf file and a number of expected boxes (k) as inputs. Note: k has no real effect.
./main.py inputs.pdf kThe outputs are written to stdout but beware, the program logs its progress to stderr.
To suppress the log outputs or write them to a specific file, you can do
./main.py inputs.pdf k 2>/path/to/log # Write to log file
./main.py inputs.pdf k 2>/dev/null # Suppress log outputsThe data that comes out of the program is json formatted. It is a single json object, containing three lists, "boxes", "image_folder" and "pages". The image folder is the folder where images are placed. It outputs a list of checkboxes it has found, with ids and coordinates. e.g.
{
"0": {
"x": 1214,
"y": 908
},
"1": {
"x": 1418,
"y": 1151
},
"2": {
"x": 845,
"y": 1373
},
}In addition, a list of all the pages, with corresponding lists of their checked boxes are outputted. e.g.
[
{
"boxes": {
"0": true,
"3": true,
"5": true,
"12": true,
"14": true,
"17": true,
"20": true,
"22": true,
"27": true,
"28": true
},
"page": 1
},
]This implementation works best if the input pdf is of some length. When the boxes are detected, they are approximated by a 4 edged polygon which has to meet some criteria to be regarde as a box (i.e. they must be mostly square). Big check marks that go outside the box boundaries often trip this detection up, meaning that for every page, a few boxes go undetected. To mitigate this, the program takes into account all the pages when determining where the boxes are, and hence the pdf must be of some size before we can ensure that all boxes are detected.
The system correctly identifies checked boxes.
We first attempted to use HoughLines to detect the boxes, but contour approximation turned out to be much more affective, as can be seen below.
Attempt 1, HoughLines:
Attempt 2, using contour detection:
