Hidden Markov Models (HMMs) have been shown to be useful in handwritten pattern recognition. However, owing to their fundamental structure, they have little resistance to unexpected noise among observation sequences. In other words, unexpected noise in a sequence might "break" the normal transmission of states for this sequence, making it unrecognizable to trained models. To resolve this problem, we propose a leave-one-out-training strategy, which will make the models more robust. We also propose a leave-one-out-testing method, which will compensate for some of the negative effects of this noise. The latter is actually an example of a system with a single classifier and multiple classifications. Compared with the 98.00 percent accuracy of the benchmark HMMs, the new system achieves a 98.88 percent accuracy rate on handwritten digits.