EAU-Net: A New Edge-Attention Based U-Net for Nationality Identification

Abstract

Identifying crime or individuals is one of the key tasks toward smart and safe city development when different nationals are involved. In this regard, identifying Nationality/Ethnicity through handwriting has received special attention. But due to freestyle and unconstrained writing, identifying nationality is challenging. This work considers words written by people of 10 nationals namely, India, Malaysia, Myanmar, Bangladesh, Iran, Pakistan, Sri Lanka, Cambodia, Palestine, and China, for identification. To extract invariant features, such as the distribution of edge patterns despite of the adverse effect of different writing styles, paper, pen, and ink, we explore a new Edge-Attention based U-Net (EAU-Net), which generates edge points for each input word image written by different nationals. Inspired by the success of the Convolutional Neural Network for classification, we explore CNN for the classification of 10 classes by considering candidate points given by EAU-Net as input. The proposed method is tested on our newly developed dataset of 10 classes, a standard dataset of 5 classes to demonstrate the effectiveness in classifying different nationalities. Furthermore, the efficacy of the proposed method is shown by testing on IAM dataset for gender identification. The results of the proposed and existing methods show that the proposed method outperforms the existing methods for both nationality and gender identification.