Integration of structural and statistical information for unconstrained handwritten numeral recognition

We propose an approach that integrates the statistical and structural information for unconstrained handwritten numeral recognition. This approach uses state-duration adapted transition probability distribution to overcome the weakness of state-duration modeling of conventional HMMs and uses macro-states to tackle the difficulty for HMMs to model pattern structures. Consequently the proposed method is superior to conventional approaches in many aspects. The experimental results show that the proposed approach can achieve high performance in terms of speed and accuracy