Deep bidirectional long short-term memory for online multilingual writer identification based on an extended Beta-elliptic model and fuzzy elementary perceptual codes

The development of pattern recognition and artificial intelligence domains owes the writer identification challenge greatly. In fact, writer identification is still a challenging task in the definition of a set of features able to characterize the various handwritten documents. These handwritten documents are not generally stable and show a wide variability from the same person over time, or from different writers. The capacity to identify the documents’ writers provides further chances of using these handwritten documents for several applications like forensic science, control access, digital rights management and financial transactions. In this paper, we propose a novel system to text-independent online multilingual writer identification. Our system is based on new model that we named the Extended Beta-Elliptic Model. Moreover, we are interested in using the Fuzzy Elementary Perceptual Codes to characterize the handwriting of writers well. In addition, we adopted the use of Recurrent Neural Network with Deep Bidirectional Long Short-Term Memory in the training and identification phases. Experiments are conducted on IBM_UB_1 and ADAB datasets with 98.44% and 100% writer identification rates respectively. The proposed system using the combination of the Extended Beta-Elliptic model and the Fuzzy Elementary Perceptual Codes in features extraction and the Deep Bidirectional Long Short-Term Memory in classification outperforms the existing online writer identification systems on both Latin and Arabic scripts.