An Effective Card Scanning Framework for User Authentication System

Exponential growth of fake ID cards generation leads to increased tendency of forgery with severe security and privacy threats. University ID cards are used to authenticate actual employees and students of the university. Manual examination of ID cards is a laborious activity, therefore, in this paper, we propose an effective automated method for employee/student authentication based on analyzing the cards. Additionally, our method also identifies the department of concerned employee/student. For this purpose, we employ different image enhancement and morphological operators to improve the appearance of input image better suitable for recognition. More specifically, we employ median filtering to remove noise from the given input image. Next, we apply the histogram equalization to enhance the contrast of the image. We employ Canny edge detector to detect the edges from this equalized image. The resultant edge image contains the broken characters. To fill these gaps, we apply the dilation operator that increases the thickness of the characters. Dilation fills the broken characters, however, also add extra thickness that is then removed through applying the morphological thinning. Finally, dilation and thinning are applied in combination to Optical character recognition (OCR) to segment and recognize the characters including the name, ID, and department of the employee/student. Finally, after the OCR application on the morphed image, we obtain the name, ID, and department of the employee/student. If the concerned credentials of the employee/student are matched with his/her department, then access of the door is granted to that employee/student. Experimental results illustrate the effectiveness of the proposed method.