Source classification using document images from smartphones and flatbed scanners

With technological advancements, digital scans of printed documents are increasingly used in many systems in place of the original hard copy documents. This convenience to use digital scans comes at increased risk of potentially fraudulent and criminal activities due to their easy manipulation. To curb such activities, identification of source corresponding to a scanned document can provide important clues to investigating agencies and also help build a secure communication system. This work utilizes local tetra patterns to capture unique device-specific signatures from images of printed documents. In this first of its kind work for scanner identification, the method uses all characters to train a single classifier thereby, reducing the amount of training data required. The proposed method depicts font size independence when tested on an existing scanner dataset and a novel step towards font shape independence when tested on a smart phone dataset of comparable size (Supplementary material and code is available at https://sites.google.com/view/manaslab).