Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   consider a hidden Markov mesh random field (HMMRF) for character recognition. The model consists of a "hidden" Markov mesh random field (MMRF) and on overlying probabilistic observation function of the MMRF. Just like the 1-D HMM, the hidden layer is characterized by the initial and the transition probability distributions, and the ovservation layer is defined by distribution functions for vector-quantized (VQ) observations. The HMMRF-based method consists of two phases: decoding and training. The decoding and the training algorithms are developed using dynamic programming and maximum likelihood estimation methods. To accelerate the computation in both phases,  employed a look-ahead scheme based on maximum marginal a posteriori probability criterion for third-order HMMRF. Tested on a largetst-set handwritten Korean Hangul character database, the model shod a promising result: up to 87.2% recognition rate with 8 state HMMRF and 128 VQ levels.
['Hee Seon Park', 'Bong K. Sin', 'Jongsub Moon', 'Seong Whan Lee']
I am writing a research paper on a idea A 2-D HMM method for offline handwritten character recognition
[{"from": "human", "value": "I am writing a research paper on a idea A 2-D HMM method for offline handwritten character recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   consider a hidden Markov mesh random field (HMMRF) for character recognition. The model consists of a \"hidden\" Markov mesh random field (MMRF) and on overlying probabilistic observation function of the MMRF. Just like the 1-D HMM, the hidden layer is characterized by the initial and the transition probability distributions, and the ovservation layer is defined by distribution functions for vector-quantized (VQ) observations. The HMMRF-based method consists of two phases: decoding and training. The decoding and the training algorithms are developed using dynamic programming and maximum likelihood estimation methods. To accelerate the computation in both phases,  employed a look-ahead scheme based on maximum marginal a posteriori probability criterion for third-order HMMRF. Tested on a largetst-set handwritten Korean Hangul character database, the model shod a promising result: up to 87.2% recognition rate with 8 state HMMRF and 128 VQ levels."}]