How to differentiate Passport and PAN card Scanned images in python - python-3.x

Question

The goal is to identify that the input scanned image is passport or PAN card using Opencv.
I have used structural_similarity(compare_ssim) method of skimage to compare input scan image with the images of template of Passport and PAN card.
But in both cases i got low score.
Here is the code that i have tried
from skimage.measure import compare_ssim as ssim
import matplotlib.pyplot as plt
import numpy as np
import cv2enter code here
img1 = cv2.imread('PAN_Template.jpg', 0)
img2 = cv2.imread('PAN_Sample1.jpg', 0)
def prepare_img(im):
size = 300, 200
im = cv2.resize(im, size)
return im
img1 = prepare_img(img1)
img2 = prepare_img(img2)
def compare_images(imageA, imageB):
s = ssim(imageA, imageB)
return s
ssim = compare_images(img1, img2)
print(ssim)
Comparing the PAN Card Template with Passport i have got ssim score of 0.12
and Comparing the PAN Card template with a PAN Card the score was 0.20
Since both the score were very close i wast not able to distinguish between them through the code.
If anyone got any other solution or approach then please help.
Here is a sample image
PAN Scanned Image

Answer 1

You can also compare 2 images by the mean square error (MSE) of those 2 images.
def mse(imageA, imageB):
# the 'Mean Squared Error' between the two images is the
# sum of the squared difference between the two images;
# NOTE: the two images must have the same dimension
err = np.sum((imageA.astype("float") - imageB.astype("float")) ** 2)
err /= float(imageA.shape[0] * imageA.shape[1])
# return the MSE, the lower the error, the more "similar"
# the two images are
return err

Answer 2

As per my understanding Pan card and Passport images contain different text data, so i believe OCR can solve this problem.
All you need to do is- extract the text data from the images using any OCR library like Tesseract and look for a few predefined key words in the text data to differentiate the images.
Here is simple Python script showing the image pre-processing and OCR using pyteseract module:
img = cv2.imread("D:/pan.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret,th1 = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)
cv2.imwrite('filterImg.png', th1)
pilImg = Image.open('filterimg.png')
text = pytesseract.image_to_string(pilImg)
print(text.encode("utf-8"))
Below is the binary image used for OCR:
I got the below string data after doing the OCR on the above image:
esraax fram EP aca ae
~ INCOME TAX DEPARTMENT Ld GOVT. OF INDIA
wrtterterad sg
Permanent Account Number. Card \xe2\x80\x98yf
KFWPS6061C
PEF vom ; ae
Reviavs /Father's Name. e.
SUDHIR SINGH : . ,
Though this text data contains noises but i believe it is more than enough to get the job done.

Answer 3

Another OCR solution is to use TextCleaner ImageMagick script from Fred's Scripts. A tutorial which explain how to install and use it (on Windows) is available here.
Script used:
C:/cygwin64/bin/textcleaner -g -e normalize -f 20 -o 20 -s 20 C:/Users/Link/Desktop/id.png C:/Users/Link/Desktop/out.png
Result:
I applied OCR on this with Tesseract (I am using version 4) and that's the result:
fart
INCOME TAX DEPARTMENT : GOVT. OF INDIA
wort cra teat ears -
Permanent Account Number Card
KFWPS6061C
TT aa
MAYANK SUDHIR SINGH el
far aT ary /Father's Name
SUDHIR SINGH
Wa RT /Date of Birth den. +
06/01/1997 genge / Signature
Code for OCR:
import cv2
from PIL import Image
import tesserocr as tr
number_ok = cv2.imread("C:\\Users\\Link\\Desktop\\id.png")
blur = cv2.medianBlur(number_ok, 1)
cv2.imshow('ocr', blur)
pil_img = Image.fromarray(cv2.cvtColor(blur, cv2.COLOR_BGR2RGB))
api = tr.PyTessBaseAPI()
try:
api.SetImage(pil_img)
boxes = api.GetComponentImages(tr.RIL.TEXTLINE, True)
text = api.GetUTF8Text()
finally:
api.End()
print(text)
cv2.waitKey(0)
Now, this don't answer at your question (passport or PAN card) but it's a good point where you can start.

Answer 4

Doing OCR might be a solution for this type of image classification but it might fail for the blurry or not properly exposed images. And it might be slower than newer deep learning methods.
You can use Object detection (Tensorflow or any other library) to train two separate class of image i.e PAN and Passport. For fine-tuning pre-trained models, you don't need much data too. And as per my understanding, PAN and passport have different background color so I guess it will be really accurate.
Tensorflow Object Detection: Link
Nowadays OpenCV also supports object detection without installing any new libraries(i.e.Tensorflow, caffee, etc.). You can refer this article for YOLO based object detection in OpenCV.

Answer 5

We can use:
Histogram Comparison - Simplest & fastest methods, using this we will get the similarity between histograms.
Template Matching - Searching and finding the location of a template image, using this we can find smaller image parts in a bigger one. (like some common patterns in PAN card).
Feature Matching - Features extracted from one image and the same feature will be recognised in another image even if the image rotated or skewed.