QUWI: An Arabic and English Handwriting Dataset for Offline Writer Identification

This paper presents a new offline dataset called the Qatar University Writer Identification dataset (QUWI). This dataset contains both Arabic and English handwritings and can be used to evaluate the performance of offline writer identification systems. It consists of handwritten documents of 1017 volunteers of different ages, nationalities, genders and education levels. The writers were asked to copy a specific text and to generate a random text, which allows the dataset to be used for both text-dependent and text-independent writer identification tasks. We describe the gathering and processing steps and define several evaluation tasks regarding the use of this dataset.