Arabic Handwriting Recognition Based on Synchronous Multi-stream HMM Without Explicit Segmentation

In this study, we propose a synchronous Multi-Stream Hidden Markov Model (MSHMM) for offline Arabic handwriting word recognition. Our proposed model has the advantage of efficiently modelling the temporal interaction between multiple features. These features are composed of a combination of statistical and structural ones, which are extracted over the columns and rows using a sliding window approach. In fact, word models are implemented based on the holistic and analytical approaches without any explicit segmentation. In the first approach, all the words share the same architecture but the parameters are different. Nevertheless, in the second approach, each word has it own model by concatenating its character models. The results carried out on the IFN/ENIT database show that the analytical approach performs better than the holistic one and the MSHMMs in Arabic handwriting recognition is reliable.