Improving Recurrent Neural Networks for Offline Arabic Handwriting Recognition by Combining Different Language Models

In handwriting recognition, the design of relevant features is very important, but it is a daunting task. Deep neural networks are able to extract pertinent features automatically from the input image. This drops the dependency on handcrafted features, which is typically a trial and error process. In this paper, we perform an exhaustive experimental evaluation of learned against handcrafted features for Arabic handwriting recognition task. Moreover, we focus on the optimization of the competing full-word based language models by incorporating different characters and sub-words models. We extensively investigate the use of different sub-word-based language models, mainly characters, pseudo-words, morphemes and hybrid units in order to enhance the full-word handwriting recognition system for Arabic script. The proposed method allows the recognition of any out of vocabulary word as an arbitrary sequence of sub-word units. The KHATT database has been used as a benchmark for the Arabic handwriting recognition. We show that combining multiple language models enhances considerably the recognition performance for a morphologically rich language like Arabic. We achieve the state-of-the-art performance on the KHATT dataset.