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This paper presents a system for offline recognition of cursive Arabic handwritten text based on Hidden Markov Models (HMMs). The proposed work reports an effective method taking into account the context of character by applying an embedded training-based HMMs to perform and enhance the character models. The system is analytical without explicit segmentation; extracted features preceded by baseline estimation are statistical and structural to integrate both the peculiarities of the text and the pixel distribution characteristics of the word image. The experiments are done on benchmark IFN/ENIT database. The proposed work shows the effectiveness of using embedded training-based HMMs for enhancing the recognition rate, and the obtained results are promising and encouraging.

Aiming at the typical structure of military aircraft avionics systems, this paper presents a design of the airborne embedded training system. In this paper, the design requirements of embedded training system are discussed, which lay the groundwork for the improvement of the airborne embedded training system design plan. The overall architecture of airborne embedded training system is designed in details. The Arena simulation software is used to simulate the integration process between airborne embedded training system and other avionics systems. The evaluation of quota system of training is formulated. VC++6.0 is used to make simulation for performance evaluation process.