Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
