Signature Detection

A simple tool to detect if there is a signature in an image or a PDF file.

Installation of PyPi

It's the quick way to use this tool.

signature-detect package contains the codes in the src.

pip install signature-detect

Installation of Anaconda

It's the recommended way to explore this tool. It provides notebooks for playing around.

1. install anaconda

2. install dependencies

   conda create --name <env> --file conda.txt
   

Demo

• Image:

  python demo.py --file my-image.jpeg
  

• PDF File:

  python demo.py --file my-file.pdf
  

Unit Tests

All the code in src is covered.

cd tests
coverage run -m unittest
coverage report -m

Example

We use the following image as an example. The full example is in the demo notebook

Loader

The loader reads the file and creates a mask.

The mask is a numpy array. The bright parts are set to 255, the rest is set to 0. It contains ONLY these 2 numbers.

Attributes

• low_threshold = (0, 0, 250)

• high_threshold = (255, 255, 255)

They control the creation of the mask, used in the function cv.inRange.

Result

Here, yellow is 255 and purple is 0.

Extractor

The extractor, first, generates the regions from the mask.

Then, it removes the small and the big regions because the signature is neither too big nor too small.

The process is as follows:

1. label the image

   skimage.measure.label labels the connected regions of an integer array. It returns a labeled array, where all connected regions are assigned the same integer value.

2. calculate the average size of the regions

   Here, the size means the number of pixels in a region.

   We accumulate the number of pixels in all the regions, total_pixels. The average size is total_pixels / nb_regions.

   If the size of a region is smaller than min_area_size, this region is ignored. min_area_size is given by the user.

3. calculate the size of the small outlier

   small_size_outlier = average * outlier_weight + outlier_bias
   

   outlier_weight and outlier_bias are given by the user.

4. calculate the size of the big outlier

   big_size_outlier = small_size_outlier * amplifier
   

   amplifier is given by the user.

5. remove the small and big outliers

Attributes

• outlier_weight = 3

• outlier_bias = 100

• amplifier = 10

  15 is used in the demo.

• min_area_size = 10

Result

Cropper

The cropper finds the contours of regions in the labeled masks and crops them.

Attributes

Suppose (h, w) = region.shape.

• min_region_size = 10000

  If h * w < min_region_size, then this region is ignored.

• border_ratio: float

  border = min(h, w) * border_ratio

  The border will be removed if this attribute is not 0.

Result

Judger

The judger reads the cropped mask and identifies if it's a signature or not.

Attributes

Suppose (h, w) = cropped_mask.shape.

• size_ratio: [low, high]

  low < max(h, w) / min(h, w) < high.

• max_pixel_ratio: [low, high]

  low < the number of 0s / the number of 255s < high.

  The mask should only have 2 values, 0 and 255.

By default:

• size_ratio = [1, 4]

• max_pixel_ratio = [0.01, 1]

Result

• max(h, w) / min(h, w) = 3.48

• number of 0s / number of 255s = 0.44

So, this image is signed.