Forgery document detection in information management system using cognitive techniques

Manually verifying the authenticity of the physical documents (personal identity card, certificates, passports, legal documents) increases the administrative overhead and takes a lot of time. Later image processing techniques were used. But most of the image processing based forgery document detection methods are less accurate. To improve the accuracy, this paper proposes an automatic document verification model using Convolutional Neural Networks (CNN). Furthermore, we use Optical Character Recognition (OCR) and Linear Binary Pattern (LBP) to extract the textual information and regional edges from the documents. Later, Oriented fast and Rotated Brief (ORB) is used to extract the images from the scanned documents. To train the CNN, MIDV-500 dataset of 256 Azerbaijani passport images, each with the size of 1040*744 pixels is taken. The proposed CNN model uses sliding window operations layers to evaluate the authenticity. The proposed model analyzes both the textual authenticity and image (seal, stamp and hologram) authenticity of the scanned document. The experimental analysis is carried out on the TensorFlow using python programming language. The results derived from the proposed CNN based forgery detection model is compared with existing models and the results are promising to implement on the real time applications