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We formulate the issue of joint image segmentation and recognition as an integrated statistical inference problem. A two-layer graphical model is proposed that supports the optimal segmentation and recognition in an unified Bayesian framework. Due to the explicit modeling of two tasks in the graphical model, an efficient non-iterative belief propagation algorithm is used for state estimation. The proposed approach is applied to automatic licence plate recognition (ALPR), and it outperforms traditional methods where the two tasks are implemented independently and sequentially.