In this paper a new on-line handwriting recognition system for Arabic personal names based on Hidden Markov Model (HMM) is presented. The system is trained with the ADAB-database using two different methods: manually segmented characters and non-segmented words. This work presents a recognition system dealing with a large vocabulary of 2800 Arabic personal names using a new lexicon reduction method that depends on the delayed strokes formation and the number of strokes. Besides, a new delayed strokes detection method is used to reduce the temporal variation of the on-line sequence. A dataset of on-line Arabic handwritten names has been collected to validate the system and a highly encouraging recognition rate is achieved compared to the results of commercially available recognition systems on the same dataset.
No takes yet. Share an insight, caveat, or question.
Abdelazeem et al. (2011) studied this question.