Combining CNNs and Corner Detection for Arabic Writer Identification

Abstract

The Arabic language occupies fifth place in the ranking of spoken languages, meaning that approximately 420 million people speak it. People have been biometrically identified using fingerprints, faces, and other similar features. In this paper, a biometric identification model for Arabic handwriting is proposed, as many Arabic letters have very similar shapes and can only be distinguished by the location of one or more dots, either above or below the letter. A novel and efficient offline Arabic handwriting identification model is presented. Its basis is the combination of several methods, such as the Harris corner detector, Shi-Thomasi, and convolutional neural networks (CNNs). Data augmentation is used during the model training phase to improve the data quality, without the need to segment words/characters. Leveraging a large collection of handwritten Arabic documents, such as KHATT and AHAWP, accuracy rates of 99% and 98% were reached, respectively.