Domain and writer adaptation of offline Arabic handwriting recognition using deep neural networks

Arabic Handwritten Text Recognition (AHTR) based on deep learning approaches remains a challenging problem due to the inevitable domain shift like the variability among writers’ styles and the scarcity of labelled data. To alleviate such problems, we investigate in this paper different domain adaptation strategies of AHTR system. The main idea is to exploit the knowledge of a handwriting source domain and to transfer this knowledge to another domain where only few labelled data are available. Different writer-dependent and writer-independent domain adaptation strategies are explored using a convolutional neural networks (CNN) and Bidirectional Long Short Term Memory (BSTM) - connectionist temporal classification (CTC) architecture. To discuss the interest of the proposed techniques on the target domain, we have conducted extensive experiments using three Arabic handwritten text datasets, mainly, the MADCAT, the AHTID/MW and the IFN/ENIT. Concurrently, the Arabic handwritten text dataset KHATT was used as the source domain. The obtained results prove the effectiveness of the proposed strategies specially when considering the writer’s information during the supervised adaptation process.