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In our article we deal with the simultaneous problem of reconstruction and recognition of binary symbols loaded with heavy additive noise. We introduce a Markov Random Field (MRF) model where a shape energy term is responsible to find a solution similar to a tested hypothesis. This way we could increase the precision of the reconstruction process the only question is how to find out the right hypotheses which helps the reconstruction the best way. Fortunately the new energy term gives us the answer: the tested hypotheses with the minimal shape energy component designates the right shape.

SIMULTANEOUS RECONSTRUCTION AND RECOGNITION OF NOISY CHARACTER-LIKE SYMBOLS